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Record W3009557578 · doi:10.18034/gdeb.v8i2.100

Impact of Management Information Systems (MIS) on Decision Making

2019· article· en· W3009557578 on OpenAlexaff
Md. Monsur Ali

Bibliographic record

VenueGlobal Disclosure of Economics and Business · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsLa Cité Collégiale
Fundersnot available
KeywordsComputer scienceManagement information systemsProcess (computing)Quality (philosophy)Information systemKnowledge managementDecision support systemBusiness decision mappingProcess managementInformation qualityInformation technologyDecision makerBusiness processRisk analysis (engineering)Management scienceBusinessEngineeringMarketingWork in processData mining

Abstract

fetched live from OpenAlex

Today’s business environment is unpredictable, dynamic, unstable and, necessitates the growing demand for accurate, relevant, complete, timely and, economical information needed to drive the decision-making process. The quick developments of information technology coupled with the development of telecommunications technologies, have modernized all areas of business and human activities. In today’s business world, there are different types of information systems. Each plays a unique role for a manager decision-making functions. In this paper, the decision maker’s satisfaction, contents of information and information access quality have been analyzed and studied. Here identified necessary variables aiming to evaluate the influence of management information systems in decision support capabilities and side by side discuss the concept, characteristics, types of MIS, the MIS model, and in particular it will highlight the impact of MIS in decision making. At the same time, different models and figures are presented to enrich the discussion and to highlight the status of each MIS and DSS information systems in an organization decision-making process.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.663
Threshold uncertainty score0.609

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 communication0.0000.002
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 head0.019
GPT teacher head0.259
Teacher spread0.240 · 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

Citations8
Published2019
Admission routes1
Has abstractyes

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