Опубликована: Окт. 25, 2024
Язык: Английский
International journal of organizational analysis, Год журнала: 2025, Номер unknown
Опубликована: Фев. 28, 2025
Purpose This study aims to identify and model deterrents adopt institutionalize analytics artificial intelligence in modern human resource (HR) using interpretive structural modelling (ISM) cross-impact matrix multiplication applied classification (MICMAC) approach. Design/methodology/approach A comprehensive investigation of the literature feedback from experts led identification 16 this study. After that, ISM tool is used find connections between identified HR ecosystem MICMAC which helps categorising on basis driving dependence power provides deeper insights into their roles significance. Findings Employee resistance transformation are highly influenced by other factors but exert minimal power. Data availability, leadership support, communication collaboration, legal, ethical regulatory compliance, infrastructure resources exhibit strong influence dependence, making them sensitive crucial. Training development, learning culture change management, data privacy security have with indicating foundational role shaping transformation. Research limitations/implications will assist policymakers owners/managers recognising comprehending importance applicability AI obstacles while developing strategies. Originality/value explicitly focuses technology current scenario. It also explores relationship powers.
Язык: Английский
Процитировано
0
International Journal of Information Technology, Год журнала: 2024, Номер 16(7), С. 4481 - 4487
Опубликована: Июль 29, 2024
Язык: Английский
Процитировано
1
ATESTASI Jurnal Ilmiah Akuntansi, Год журнала: 2024, Номер 7(1), С. 525 - 560
Опубликована: Март 31, 2024
This study aims to investigate the key determinants of Human Resource Management (HRM) effectiveness and their implications for organizational financial performance. A structured literature review methodology was employed synthesize findings from various academic databases sources, including Google Scholar, JSTOR, ScienceDirect, business-focused like Business Source Premier. Keywords such as "human resource management," "organizational performance," "talent "training development," "performance management" were used refine search. The focused on theoretical frameworks Resource-Based View, Capital Theory, Strategic HRM Model, Social Exchange Contingency well empirical studies examining relationship between practices outcomes. suggest that strategic alignment strategy is crucial enhancing performance, along with effective talent management, training development initiatives, performance management systems. Moreover, culture impact technology identified important moderating mediating factors influencing practices. underscores need practitioners align strategy, invest in development, leverage drive success. synthesis research provides valuable insights both scholars seeking understand enhance achieving
Язык: Английский
Процитировано
1
Опубликована: Авг. 8, 2024
Язык: Английский
Процитировано
0
International Journal of Applied Research, Год журнала: 2024, Номер 10(4)
Опубликована: Янв. 1, 2024
This research paper delves into the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM), specifically focusing performance evaluation and employee engagement.The primary objective this study is to explore how AI technologies, such as machine learning natural language processing, can enhance accuracy fairness evaluations, they be leveraged improve engagement within organizations.Through a systematic literature review, synthesizes findings from recent papers case studies assess effectiveness tools in HRM practices.The methodology employed involves comprehensive analysis peer-reviewed articles, conference proceedings, empirical published between 2015 2023.Key indicate that significantly contributes by providing data-driven insights ensure objectivity.Furthermore, AI-driven are found instrumental enhancing facilitating real-time feedback personalized strategies.However, integration also presents challenges, including concerns related privacy, bias, need for human oversight.The concludes with practical recommendations HR professionals aiming implement practices, emphasizing importance ethical considerations element technology adoption.This theory highlighting role evolving practices offers future work era.
Язык: Английский
Процитировано
0
International Journal of Applied Research, Год журнала: 2024, Номер 10(4)
Опубликована: Апрель 1, 2024
This research paper delves into the transformative impact of Artificial Intelligence (AI) on Human Resource Management (HRM), specifically focusing performance evaluation and employee engagement.The primary objective this study is to explore how AI technologies, such as machine learning natural language processing, can enhance accuracy fairness evaluations, they be leveraged improve engagement within organizations.Through a systematic literature review, synthesizes findings from recent papers case studies assess effectiveness tools in HRM practices.The methodology employed involves comprehensive analysis peer-reviewed articles, conference proceedings, empirical published between 2015 2023.Key indicate that significantly contributes by providing data-driven insights ensure objectivity.Furthermore, AI-driven are found instrumental enhancing facilitating real-time feedback personalized strategies.However, integration also presents challenges, including concerns related privacy, bias, need for human oversight.The concludes with practical recommendations HR professionals aiming implement practices, emphasizing importance ethical considerations element technology adoption.This theory highlighting role evolving practices offers future work era.
Язык: Английский
Процитировано
0
Molecular & cellular biomechanics, Год журнала: 2024, Номер 21, С. 118 - 118
Опубликована: Авг. 19, 2024
Gas concentration level prediction in coal mines is a challenging task due to the complex environment and high risk of gas explosion. Traditional methods rely on manual monitoring experience, which may result inaccurate predictions even accidents. In recent years, neural network (NN) models have been applied prediction, showing promising results. This paper aims investigate effectiveness NN multiple mine stations. A dataset measurements five stations used train evaluate models. We evaluated model testing set obtained an accuracy 95.2% for methane 94.8% carbon monoxide prediction. Results show that achieves can be as reliable tool safety management.
Язык: Английский
Процитировано
0
Lecture notes in networks and systems, Год журнала: 2024, Номер unknown, С. 365 - 373
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0
SSRN Electronic Journal, Год журнала: 2024, Номер unknown
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
0
Опубликована: Окт. 25, 2024
Язык: Английский
Процитировано
0