The Entropic Complexity of Human Factor in Collaborative Technologies

Panagou, Sotirios; Fruggiero, Fabio; Neumann, Patrick; Lambiase, Alfredo

doi:10.1007/978-3-030-74608-7_62

Cited by 4 publications

(2 citation statements)

References 13 publications

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“…This can include selecting relevant training materials, determining the most effective learning format, or crafting a career path that suits employee goals and aspirations (Gan, 2020). By building employee development through Big Data, organizations can create an environment that supports professional and personal growth (Panagou et al, 2021). Careful application of the insights provided by Big Data can help shape a progressive learning culture and ensure that investments in employee development produce significant positive impacts (Ferket, 2019).…”

Section: Employee Developmentmentioning

confidence: 99%

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Heliana,

Wahyuni

2024

jrmsi

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This research aims to conduct an in-depth analysis of the application of Big Data in human resource decision-making, focusing on optimizing workforce management. By measuring four main variables, namely recruitment efficiency, accuracy of employee selection, employee development, and organizational performance based on HR decision-making—Big Data analysis is expected to provide a deeper understanding of workforce dynamics. The research results show that applying Big Data can build recruitment efficiency, increase the accuracy of employee selection, support more appropriate employee development, and ultimately improve overall organizational performance. Big Data makes organizational performance based on HR decision-making by providing a solid foundation for more effective human resource management strategies. This research uses qualitative methods with the help of the NVivo 12 Plus tool to identify research results. Implementing Big Data in various aspects of human resource management is recognized as a catalyst for positive change in workforce management.

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Section: Employee Developmentmentioning

confidence: 99%

Big Data Analysis in Human Resources Decision Making: Optimizing Workforce Management

Heliana,

Wahyuni

2024

jrmsi

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“…A growing number of research papers refer to the concept of Industry 5.0. Industry 5.0 focuses, among other things, on the sustainability of the human factor inside the technologies of Industry 4.0 (Panagou et al, 2021). The major point would be to focus on the human and the environment with Industry 5.0 and assist repetitive tasks with, e.g., robots, exoskeletons, or machines to enable the human workforce to focus on more complex tasks in the future (Nahavandi, 2019).…”

Section: The Digital Factorymentioning

confidence: 99%

Extending and demonstrating an engineering communication framework utilising the digital twin concept in a context of factory layouts

Syberfeldt¹,

Lämkull²,

Högberg³

et al. 2023

IJSOI

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The factory layout is frequently planned in virtual environments, based on the experience of software tool users. This planning process is cumbersome and iterative to collect the necessary information, with a high risk of faulty inputs and updates. The digital twin concept has been introduced in order to speed up information sharing within a company; it relies on connectivity. However, the concept is often misunderstood as just a 3D model of a virtual object, not including connectivity. The aim of this paper is to present an extended virtual and physical engineering communication framework including four concepts: digital model, digital pre-runner, digital shadow, and digital twin. The four concepts are demonstrated and described in order to facilitate understanding how data exchange between virtual and physical objects can work in the future and having up-to date virtual environments enables simulating, analysing, and improving on more realistic and accurate datasets.

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