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Record W4391777290 · doi:10.2478/ijme-2023-0027

Editorial

2023· editorial· en· W4391777290 on OpenAlexaboutno aff
Mariusz Próchniak

Bibliographic record

VenueInternational Journal of Management and Economics · 2023
Typeeditorial
Languageen
FieldDecision Sciences
TopicImpact of AI and Big Data on Business and Society
Canadian institutionsnot available
Fundersnot available
KeywordsBusiness managementBusinessBusiness administration

Abstract

fetched live from OpenAlex

Welcome to the fourth issue of the International Journal of Management and Economics in 2023.In this issue, we present six empirical papers written by authors representing numerous Polish universities and institutions.In the first paper, Katarzyna Byrka-Kita, Mateusz Czerwiski, and Aurelia Bajerska investigate the link between the rule of law and equity returns in posttransitional economies.By applying several rule of law proxies for national legal frameworks and justice system quality as proxies of the rule of law principle, they show that the country-level judicial system quality is an important driver of company market performance and that posttransition countries with lower rule of law measures exhibit higher returns on equity than those with better measures.Their results support the idea that since poor governance and country instability increase agency and transaction costs and decrease growth prospects and profitable projects available to companies, the risk premium demanded by investors increases, leading to higher equity returns.The next paper was prepared by Tomasz Berent and Maciej niechowski.The authors validate the existence of an extensively documented secular upward trend in corporate cash holding.They find no trace of a trend for Poland and believe most trends for the United States come from the cash piling toward the end of the sample period.At best, the U.S. trend applies merely to small firms.The authors believe cash holding is a period-dependent time-varying variable that also depends on external shocks (e.g., the pandemic or tax regulations).They show that a simple addition of macro data (e.g., GDP) vastly improves models focused only on optimal cash holding and firm-specific characteristics.The third paper by Micha Comporek aims to analyze the earnings quality of high-share liquidity companies from Poland, Romania, and Hungary whose activities are outside the finance sector.The author assesses earnings quality (earnings persistence, predictability, and accruals quality) using the Kruskal-Wallis test, U Mann-Whitney test, Wilcoxon signed ranks test, and Spearman rank correlation coefficients.The paper demonstrates that companies listed on the Bucharest Stock Exchange tend to provide higher earnings quality than other firms in the CEEplus index.There was a noticeable domination of managerial practices aimed at managing the earnings downward.This also happened in 2020, i.e., the period negatively affected by the COVID pandemic.In the fourth paper, Dominika Brzda-Wilamek aims to evaluate the topology properties (the geographical and sectoral structure) of the global cross-border mergers and acquisitions (CBM&A) network.A quantitative study is conducted by using the social network analysis (SNA) method.The article shows that in 1990-2021, the United States, the United Kingdom, Germany, Canada, and France occupied the most central place in the network.From the beginning of the 21st century, there has also been a marked increase in the importance of Asian countries, with China and India receiving a large inflow of foreign capital.In turn, entities from Hong Kong, Singapore, Japan, and China heavily invested abroad through M&A.From a sectoral perspective, mainly entities that operated in financial, industrial, basic materials, technology, and consumer cyclical sectors made transactions in the global CBM&A network.

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)0.003
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Editorial · Consensus signal: Editorial
Teacher disagreement score0.024
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0030.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0020.000
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 head0.055
GPT teacher head0.359
Teacher spread0.304 · 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.

Study designNot applicable
Domainnot available
GenreEditorial

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
Published2023
Admission routes1
Has abstractyes

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