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Innovation, Artificial Intelligence in Contingent Work-Force Management
571-590Views:669In recent years, the global use of contingent workers is rapidly increasing despite the increasing quantity of artificial intelligence applications in business. The question is "how these companies leverage the use of artificial intelligence to enhance contingent workforce's management?". The ideal goal of this paper is to develop a purely conceptual application of innovation, artificial intelligence (AI) adjacent to contingent workforce management(CWM). The researcher used qualitative information gathered from various authors and observations to reinforce the usage of AI. One of the critical tools to integrate with contingent workforce management for reduction of time spent on human resource administrative tasks is AI. There must be a transformation of thinking, accepting positive organizational change, utilization of technology and openness to new technology to foster AI. Along with that, integrating contingent workforce management with AI reduces risks and costs, increases efficiency and quality of work. Innovation and Artificial intelligence have been used in five pillars performance of contingent workforce management to mitigate the challenges associated with it.
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Artificial Intelligence in Human Resources Information Systems: Investigating its Trust and Adoption Determinants
749-765Views:3620With the rapidly emerging trend of employing Artificial Intelligence technologies within modern economics. This study is an attempt to fill the research gap associated with the factors that have influence with the adoption of artificial intelligence in human resources information systems on HR-leaders intention to use it. It empirically investigates the influences that trust, technological readiness, facilitating condition and performance expectancy on HR-professional’s behavioral intention to use AI in HRM. Besides, examine the moderating effect of age and experience on the proposed associations. Data were collected from by online questionnaire from 185 HR managers. A structural framework was introduced to test the relationship between study latent variables. Result exhibited that trust and performance expectancy has a significant influence on HR-professionals behavioral intention to use AI-HRIS. Trust and technological readiness showed a significant influence on HR-professionals performance expectancy of using AI-HRIS. While facilitating condition, organizational size and technological readiness did not show a significant influence on HR-professionals behavioral intention toward using AI-HRIS. Lastly, Age and Experience did not have a moderating effect on trust and performance expectancy association with the behavioral intention toward using AI-HRIS. The findings of this study contribute to the theory development of information technology diffusion in HRM.
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A Literature Review: Artificial Intelligence Impact on the Recruitment Process
108-119Views:13694This paper aim is to review the implementation of artificial intelligence (AI) in the Human Resources Management (HRM) recruitment processes. A systematic review was adopted in which academic papers, magazine articles as well as high rated websites with related fields were checked. The findings of this study should contribute to the general understanding of the impact of AI on the HRM recruitment process. It was impossible to track and cover all topics related to the subject. However, the research methodology used seems to be reasonable and acceptable as it covers a good number of articles which are related to the core subject area. The results and findings were almost clear that using AI is advantages in the area of recruitment as technology can serve best in this area. Moreover, time, efforts, and boring daily tasks are transformed to be computerized which makes a good space for humans to focus on more important subjects related to boosting performance and development. Acquiring automation and cognitive insights as well as cognitive engagement in the recruitment process would make it possible for systems to work similarly to the human brain in terms of data analysis and the ability to build an effective systematic engagement to process the data in an unbiased, efficient and fast way.
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Artificial Intelligence Possibilities in Vehicle Industry
148-154Views:302There have been several attempts during the last decades to extend the ranges of application of artificial intelligence. The aim of the development for AI is to replace human intelligence and experience. The ultimate aim for machines and vehicles is to run much more efficiently and with higher reliability than ever before. The Artificial Techniques (AI) used a wide range of expert systems to optimize problems. Hybrid intelligent management systems have become increasingly influential in artificial intelligence during the last decades. As a result, maintenance and fleet management systems have undergone significant development. By choosing adequate maintenance or operating strategy and taking user behaviour into consideration, these systems can not only increase the reliability and efficiency of vehicles but can also result in financial savings. The paper tries to discusses the applications of AI techniques in predictive maintenance and vehicle industry.
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LSI with Support Vector Machine for Text Categorization – a practical example with Python
18-29Views:401Artificial intelligence is becoming a powerful tool of modernity science, there is even a science consensus about how our society is turning to a data-driven society. Machine learning is a branch of Artificial intelligence that has the ability to learn from data and understand its behavers. Python programming language aiming the challenges of this new era is becoming one of the most popular languages for general programming and scientific computing. Keeping all this new era circumstances in mind, this article has as a goal to show one example of how to use one supervised machine learning method, Support Vector Machine, and to predict movie’s genre according to its description using the programming language of the moment, python. Firstly, Omdb official API was used to gather data about movies, then tuned Support Vector Machine model for Latent semantic indexing capable of predicting movies genres according to its plot was coded. The performance of the model occurred to be satisfactory considering the small dataset used and the occurrence of movies with hybrid genres. Testing the model with larger dataset and using multi-label classification models were purposed to improve the model.
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The Impact of Optical Character Recognition Artificial Intelligence on the Labour Market
9-16Views:374Because of present day information technology, there is neither need to plant complicated computers for more millions price if we would like to process and store big amounts of data, nor modelling them. The microprocessors and CPUs produced nowadays by that kind of technology and calculating capacity could not have been imagined 10 years before. We can store, process and display more and more data. In addition to this level of data processing capacity, programs and applications using machine learning are also gaining ground. During machine learning, biologically inspired simulations are performed by using artificial neural networks to able to solve any kind of problems that can be solved by computers. The development of information technology is causing rapid and radical changes in technology, which require not only the digital adaptation of users, but also the adaptation of certain employment policy and labour market solutions. Artificial intelligence can fundamentally question individual labour law relations: in addition to reducing the living workforce, it forces new employee competencies. This is also indicated by the Supiot report published in 1998, the basic assumption of which was that the social and economic regulatory model on which labour law is based is in crisis.
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Labour Economics - From the Technological Development Perspective
98-108Views:72The impact of technological advancements on the labor market and innovation processes is a critically important research area. The aim of this study is to examine the emergence and frequency of technological innovations in scientific publications, with a particular focus on the Journal of Labour Economics from 2000 to 2020. The research employs content analysis methods, searching for eight different terms and expressions related to technological development (e.g., technology, artificial intelligence, machine learning) across 1405 articles. The study also analyzes the number of occurrences and annual publication trends of these terms. A total of 9469 instances were identified, indicating that in 64,7% of the cases, at least one technological term appeared. An analysis of annual trends reveals an increase in the usage of certain keywords (technology, artificial intelligence, and machine learning). In a smaller subset of articles, only 1%, technological terms were mentioned at least 50 times. The results suggest that although the topic of technological development plays a significant role in labor market research, the frequency of its appearance and the depth of analysis vary considerably. The increase in the appearance of technological terms is predominantly observed in the fields of artificial intelligence and machine learning. These findings are specific to a single journal, indicating the need for further research involving other labor market journals to ensure representativeness.
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Battery Measurement Methods and Artificial Intelligence Applied in Energy Management Systems
428-436Views:155Diagnostics of batteries using advanced methods have gained remarkable roles in the past few years. This study focuses on the type of measurements, tests and methods to reveal and classify them. During manufacturing and operation several faults could emerge in batteries including non-optimal operation conditions, operators without experience, and finally, random changes in batteries under physical and nonphysical conditions. Improper handling of batteries and battery cells man cause operation failures or, in the worst case, accidents. To reveal these problems several methods are applied in industry and in scientific laboratories. For a comprehensive analysis of battery management, artificial intelligence and Industry 4.0 methods can be used very effectively. Big Data analysis in its standard form is not a new achievement, but other mathematical tools could be applied to control monitoring such as Fuzzy Logic or Support Vector Machine (SVM). They are efficient tools to analyse the deviation of batteries condition because it can detect sudden changes, parameter deviations and anomalies, and the user’s behaviour and habits. This article gives a description about the most important battery testing methods and the connection between Big Data and Operation Management with Artificial Intelligent (AI) methods.
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Trends in the Internet of Things (IoT) and Influence on the Industries’ Progress
176-187Views:200This research aims to investigate the critical role of the Internet of Things in the future of industries’ progress. For this purpose, a survey of 250 top managers across 13 industries has conducted. The objective was to find their view of point about what short and mega trends, in which sector will have the most considerable influence in the five years as well as 30 years ahead. Moreover, various technologies are also identified that will have the most importance in the future according to the majority of the respondents, such as Internet of things, Automation and Artificial Intelligence, and, on the other hand, the segments that capital expenditure is currently being directed towards, such as Energy Efficiency and Personalisation of Services.
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What Drives The Diffusion of AI Recruitment Systems in Swiss HRM? The Importance of Technological Expertise, Innovative Climate, Competitive Pressure, Employees’ Expectations and Contextual Factors
1-43.Views:30This study examines organizational, environmental, and contextual factors influencing the diffusion of artificial intelligence recruitment systems in human resources management within Swiss organizations. Based on a survey provided to 324 private and public Swiss HR professionals, it explores how some technology-organization-environment theoretical framework predictors' as well as innovative climate provided by organizations influence the three stages – evaluation, adoption, and routinization – of diffusion of this innovation. To do this, the following article is based on a PLS-SEM structural equation model. Its main findings are that technological expertise, innovative climate, competitive pressure, and expectations regarding future use of the tool by organizations working in the same field are directly linked to the spread of this type of AI tool. However, public-sector organizations are more reluctant about using this type of tool. This aversion can, however, be moderated by an innovative climate and the fact that the HR function plays an active part in an organization's strategic direction. This said, this article makes a significant contribution to the literature about the diffusion of emerging technologies in organizations.
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The Role of Industry 4.0 and Digitalization in agriculture, Especially in Romanian Agriculture
Views:422The use of robotics, automation, big data, artificial intelligence are growing in the world and in the agricultural sector, which contribute to the development of a more efficient agricultural sector. In the agriculture sector for sustainable development it is necessary the use of opportunities and technologies provided by industry 4.0. For the agriculture sector digitalization means the future, because it helps increasing output meanwhile environmental pressure is remitting, and is not increasing. The aim of these paper is to present the concept of Industry 4.0 in agriculture and to analyse the romanian agricultural sector attitude and conditions towards digitalization.
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Analysis of the Causes and Effects of Noise from Rail Transport
116-130Views:119Noise emissions from rail transport are a major concern, as they affect both the environment and people's health and quality of life. Among the many sources of noise emissions, rail vehicles and infrastructure are a major factor. With regard to rail noise emissions, it can be concluded that noise effects are influenced by a number of factors. These factors include train speed, track condition, traction technology and the noise abatement methods used. The negative effects of noise exposure include sleep disturbance, stress and mental health deterioration. It also affects the quality of life of people in urban areas and property prices. It should be emphasised that reducing noise emissions from rail transport is key to creating a healthier and more sustainable urban environment. To achieve this, it is important to use modern noise abatement technologies, improve infrastructure and implement noise abatement actions effectively. Transport authorities and railway companies should work together to achieve a more noise-free rail transport, to improve people's quality of life and to protect the environment.
