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Record W2042429260 · doi:10.5267/j.msl.2013.02.003

A study on relationship between information technology facilities and performance of banking industry

2013· article· en· W2042429260 on OpenAlexvenueno aff
Mohammad Khodaei Valahzaghard, A. Shakourloo

Bibliographic record

VenueManagement Science Letters · 2013
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsnot available
Fundersnot available
KeywordsBanking industryBusinessInformation technologyIndustrial organizationMarketingOperations managementKnowledge managementComputer scienceFinanceEconomics

Abstract

fetched live from OpenAlex

The recent advances on information technology have made tremendous change on traditional banking. These days, people do not carry cash and prefer to use electronic devices such as point of sale system (POS) or PIN entry devices (PinPad) to do desirable transactions. These technologies could facilitate e-business and increase profitability in various industries including banking sector. The purpose of this paper is to investigate the effect of five new products namely ATM, POS Machines, PinPad machines, online and swift branches on banking performance indicators including return on assets (ROA), return on equities (ROE) and operating investment return (OIR). We use the information of 19 private and governmental banks, which were active in Iran over the period of 2005-2010. The study uses linear regression analysis as well as VAR technique to study the effects of the independent variables on bank performance indicators. The results indicate that while there are some weak and positive relationships between three technology indicators including POS, PinPad and online businesses and ROA as well as ROE, there is relatively strong and positive relationship between these three independent variables and OIR. In addition, while the results of VAR analysis have shown that any reduction on PinPad will reduce OIR but this reduction will disappear after approximately four periods.

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 score0.021
Threshold uncertainty score0.787

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.011
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 head0.027
GPT teacher head0.208
Teacher spread0.181 · 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

Citations7
Published2013
Admission routes1
Has abstractyes

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