Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 103 - 111
Опубликована: Янв. 1, 2024
Язык: Английский
International Journal of Scientific Research in Computer Science Engineering and Information Technology, Год журнала: 2025, Номер 11(1), С. 2064 - 2071
Опубликована: Фев. 10, 2025
Artificial Intelligence and digital transformation have revolutionized traditional business operations, resulting in enhanced performance management systems. Performance systems are frequently perceived as fundamental compliance initiatives. However, the efficacy of a system can significantly contribute to human resource capital investment cost reduction. It is important invest an effective that enable continuous monitoring employee engagement attrition risk. This study aims provide analysis how (AI) AI-based products drive efficiency within process. improve transparency, reduce bias insights for managers leading improved decision-making. The necessity replace legacy with contemporary AI-driven benefits organizations both operationally strategically.
Язык: Английский
Процитировано
1
European Journal of Business Management and Research, Год журнала: 2025, Номер 10(1), С. 44 - 55
Опубликована: Янв. 23, 2025
The rapid development of generative artificial intelligence (AI) has led to the recognition tools like ChatGPT and its potential transform human resource (HR) management processes, particularly in decision-making. This review study aims assess effectiveness benefits enhancing HR functions, decision-making, identify any challenges ethical considerations involved. Additionally, seeks establish a hybrid framework that combines AI-driven decision-making with oversight. A systematic literature was conducted using PRISMA guidelines, selecting 50 articles from Scopus Google Scholar databases. includes synthesis analysis publication trends keyword key themes such as ChatGPT’s impact on management. reveals can streamline improve communication, support personalized learning eventually contributing enhanced performance engagement. However, technology requires input for moral judgment empathy, presenting resistance adoption, algorithmic bias, data privacy concerns. uniquely contributes by providing role proposing addresses AI’s limitations through guidelines findings emphasize need empirical research larger, diverse settings future enhancements contextual understanding HR.
Язык: Английский
Процитировано
0
Опубликована: Янв. 1, 2025
Процитировано
0
Опубликована: Апрель 24, 2025
Язык: Английский
Процитировано
0
Asian Journal of Logistics Management, Год журнала: 2024, Номер 3(2), С. 104 - 125
Опубликована: Ноя. 3, 2024
This paper examines the use of generative AI in human resource management (HRM), emphasizing improvement operational efficiency and decision-making processes. The study used a literature based approach, combining information from peer reviewed journals, books, research articles industry reports to examine adoption into HR tasks, such as recruiting, employee engagement, performance management. demonstrates that significantly enhances recruiting by decreasing time hire more precisely matching applicants with job specifications. Moreover, AI-driven technologies strengthen engagement personalizing interactions automating routine enabling professionals concentrate on key objectives.The study's uniqueness is its thorough assessment ethical dilemmas challenges related AI, including algorithmic bias privacy issues. To address these dangers, emphasizes need include justice openness deployment. results indicate while has potential for significant improvements, governance essential appropriate use.For strategic workforce management, managers must also being aware constraints. However, there are certain limitations, relying solely current biases inherent sources. Subsequent needs empirical validation formulation frameworks direct implementation resources. offers comprehensive view advantages obstacles associated integration HRM, highlighting responsible balanced implementation.
Язык: Английский
Процитировано
3
Journal of Computer Information Systems, Год журнала: 2024, Номер unknown, С. 1 - 14
Опубликована: Окт. 25, 2024
In the evolving digital work environment, rising prevalence of generative AI tools presents a complex challenge for practitioners: deciding whether to allow or restrict their use in organizational settings. Our research contributes expanding broaden-and-build theory and job demands-resources model (JD-R) within context usage. This study investigates impact on employees' perceived overload, focusing mediating role employee adaptability. Utilizing survey 307 employees Structural Equation Modeling (SEM) techniques, findings show that usage by not only directly reduces overload but also significantly boosts adaptability, further decreasing overload. highlights dual benefits workplace, offering valuable insights managers consciously integrating these technologies enhance adaptability reduce workload stress.
Язык: Английский
Процитировано
2
International Journal of Human-Computer Interaction, Год журнала: 2024, Номер unknown, С. 1 - 16
Опубликована: Авг. 9, 2024
This paper investigates the impact of usage generative AI (GenAI) and services with integrated GenAI on employee performance, alongside role trusting in these tools services. Employing a mixed methodology, research first analyzes data from 251 professionals Spain using structural equation modeling (SEM) approach, followed by qualitative survey 69 top academics management sciences. Findings indicate that adoption effective use does not directly improve workplace performance. Instead, an optimal level trust plays critical mediating role, enhancing work engagement thereby The study draws reviewed job demand-resources theory (JD-R) to construct new theoretical framework applied services, offering insights into how user experience influence productivity. For managers, results highlight importance building among employees users boost
Язык: Английский
Процитировано
2
Deleted Journal, Год журнала: 2024, Номер unknown
Опубликована: Дек. 3, 2024
Abstract The explosive advancement of contemporary artificial intelligence (AI) technologies, typified by ChatGPT, is steering humans towards an uncontrollable trajectory to general (AGI). Against the backdrop a series transformative breakthroughs, big tech companies such as OpenAI and Google have initiated “AGI race” on supranational level. As technological power becomes increasingly absolute, structural challenges may erupt with unprecedented velocity, potentially resulting in disorderly expansion even malignant development AI technologies. To preserve dignity safety human-beings brand-new AGI epoch, it imperative implement regulatory guidelines limit applications within confines human ethics rules further counteract potential downsides. promote benevolent evolution AGI, principles Humanism should be underscored connotation Digital enriched. Correspondingly, current paradigm for generative also overhauled under tenet adapt quantum leaps subversive shifts produced future. Positioned at nexus legal studies, computer science, moral philosophy, this study therefore charts course synthetic regulation framework Humanism.
Язык: Английский
Процитировано
2
ACM SIGMIS Database the DATABASE for Advances in Information Systems, Год журнала: 2024, Номер 55(3), С. 6 - 11
Опубликована: Июль 31, 2024
Generative artificial intelligence (AI) represents a crucial subset of AI models characterized by their ability to generate new content based on user input, showing vast potential transform learning and teaching. However, educators have raised ethical concerns, particularly regarding the adverse effect students' if students simply parrot generative AI-generated without engaging in critical analysis or original thought. Moreover, there exists perpetuate existing biases training data. This editorial discusses three major concerns use education proposes questions (on task-AI fit people-AI fit) approaches address considerations adopting five principles ethics. The also developing classroom policy as one governance mechanism for promoting AI. As technology continues evolve, so must our educational practices. ends with call readers (educators) collaboratively define terms engagement settings begin this discourse sharing insights experiences
Язык: Английский
Процитировано
1
Lecture notes in computer science, Год журнала: 2024, Номер unknown, С. 103 - 111
Опубликована: Янв. 1, 2024
Язык: Английский
Процитировано
1