MétaCan
Menu
Back to cohort
Record W4292522193 · doi:10.5912/jcb1299

Impact of information technology on accounting and finance in the digital health sector

2022· article· en· W4292522193 on OpenAlexaff
Muhammad Talha, Wang Fei, Darchia Maia, Goodwin Marra

Bibliographic record

VenueJournal of Commercial Biotechnology · 2022
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicFinTech, Crowdfunding, Digital Finance
Canadian institutionsUniversity of Saskatchewan
Fundersnot available
KeywordsAccountingDescriptive statisticsFinanceBusinessAccounting information systemReturn on equityReturn on assetsInformation technologyFinancial accountingEconomicsProfitability indexComputer scienceStatistics

Abstract

fetched live from OpenAlex

Determination of the impact of information technology on the field of finance as well as accounting is the main aim of this research. The study in this research paper is based on secondary data analysis; this study was conducted in Pakistan to gather the research data using different websites including world development indicators and used financial reports of health sector companies. Information technology is the main independent variable; it includes scientific practices, business practices, and cultural practices; these are all independent variables. The accounting and finance included return on assets, return on equity, monitory unit policy, revenue remuneration, commercial mortgage, invoice financing, and pension-led funding. These are all considered dependent variables. For measuring the research study, E views software and run different results such as descriptive statistic, cross-covariance, unit root test analysis, and the histogram and state. The result presents that variance ratio analysis of each indicator's overall result found that there are positive and more significant influences of modern information technology on finance and accounting. Therefore, the technology that is playing a vital role in accounting and finance departments is information technology.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.640
Threshold uncertainty score0.410

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.002
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.011
GPT teacher head0.248
Teacher spread0.237 · 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

Citations20
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueJournal of Commercial BiotechnologySame topicFinTech, Crowdfunding, Digital FinanceFrench-language works237,207