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Human Resource Management in the Digital Age: Big Data, HR Analytics and Artificial Intelligence
Published in Pedro Novo Melo, Carolina Machado, Management and Technological Challenges in the Digital Age, 2018
Mark L. Lengnick-Hall, Andrea R. Neely, Christopher B. Stone
Ethical data in an HRM context means that it is collected with consent and used appropriately to manage people and make business decisions. Universities require their researching faculties to submit proposed studies to an institutional review board (IRB) to ensure that the potential for harm to participants is minimised. Researchers are trained in what practices are acceptable, and their proposed research studies are subjected to peer review before receiving approval. However, organisations rely more on professional behaviour and self-monitoring when doing similar research, but focused on organisational needs. With more access to personal data, even medical information provided by such devices as Fitbits that monitor health status, the potential for abuse seems large. Three issues are extremely important in collecting data from employees: (1) privacy/confidentiality – individuals should have the ability to manage the flow of private information used by organisations, (2) transparency – individuals should be able to see how their data will be used and (3) informed consent – individuals should have the ability to either opt in or opt out of data collection on them. Guenole and Ferrar (2014) summarised these requirements of HR data collected on individuals: (1) providing feedback about the data from those who are affected, (2) giving individuals the option to share personal data or relying on an opt-in policy, (3) recognising the benefits to those affected and (4) ensuring transparency in data collection (or FORT for short).
Data collection and conversion
Published in Catherine Dawson, A–Z of Digital Research Methods, 2019
Data collection and conversion refers to the process of bringing together or gathering data and converting them from one media form, or format, to another (through export or data translation software, for example). This is done to enable interoperability so that computer systems or software can exchange data and make use of data (data formatting and the interoperability of GIS datasets from different sources, for example: see Chapter 21). Data capture is a term that is sometimes used interchangeably with data collection: however, data capture refers more specifically to the process of a device reading information from one system or media source and transporting it directly to another system or media source (bar code readers or intelligent document recognition, for example). Data collection is a more general term that refers to gathering information in various ways from various sources and delivering it to a system or media source (and can, therefore, include data capture techniques). Similarly, data migration is a term that is sometimes used interchangeably with data conversion. However, data migration refers specifically to the process of transferring data from one system to another, whereas data conversion refers to the process of transforming or translating data from one format to another. Data conversion is used to extract data from one source, translate or transform them, and prepare them for loading onto another system or media source (ensuring that all data are maintained, with embedded information included in the conversion: the target format must be able to support all the features and constructs of the source data).
Artificial Intelligence for Sustainable Pedagogical Development
Published in Sam Goundar, Archana Purwar, Ajmer Singh, Applications of Artificial Intelligence, Big Data and Internet of Things in Sustainable Development, 2023
S. Gomathi alias Rohini, S. Mohanavel
AI in education needs policy support with equity and inclusion. AI requires infrastructure and an ecosystem of thriving innovators. Education leaders should be aided financially and ethically to focus on shaping learners who have the skills to thrive in the AI society. The technologies covered for capturing data might be costly for some countries. In data collection, storage and processing, data ethics like ownership, accountability, transparency, bias, privacy, security and quality must be maintained.
An analysis of used lubricant recycling unit in Turkey
Published in Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019
Serap Ulusam Seçkiner, Al Mothana Al Shareef
Lubricant oil recycle system process parameters work on various temperature values between 380°C and 400°light gas oil and vacuum values between 656 and 730 mmHg. We can classify them into two main groups: temperature and vacuum as independent variables, and quantity of each distillate (HGO, MGO, and LGO) or quality of each distillate (HGO, MGO, and LGO) as dependent variables. Data collection can be defined as the process of collecting and gathering information and data. The data is then measured based on targeted variables in a systematic way. However, data collection involves humans and machines and both can be prone to errors. The alternative model development paradigm is based on developing relations based on process data. Inputoutput models are much expensive to develop. However, they only describe the relationships between the process inputs and outputs, and their utility is limited to features that are included in the available datasets (Cinar, Palazoglu, and Kayihan 2007). Therefore, data collected by means of experimental methods provide a ground for establishing models for the chemical processes in this research.