Big Data in Organizations and the Role of Human Resource Management

Scholz, Tobias

doi:10.3726/b10907

Cited by 47 publications

(22 citation statements)

References 392 publications

(568 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Even if AI/ML algorithms did not produce better employees, they lend potentially greater decision-making speed and efficiency than traditional assessments, saving organizations time and money. These savings may be the primary bottom-line improvement, such that AI/ML tools are actually changing or replacing the work of HR as much or more than their intended effect of changing the quality of hired applicants (Scholz, 2017). Regarding this latter effect, as we noted earlier, any improvements in selection accuracy from AI/ML over traditional methods remain a largely open question, and more evidence seems necessary in this space.…”

Section: Organizational Benefits Of Ai/mlmentioning

confidence: 91%

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Gonzalez

Capman²,

Oswald

et al. 2019

PAD

View full text Add to dashboard Cite

Artificial intelligence (AI) and machine learning (ML) have seen widespread adoption by organizations seeking to identify and hire high-quality job applicants. Yet the volume, variety, and velocity of professional involvement among I-O psychologists remains relatively limited when it comes to developing and evaluating AI/ML applications for talent assessment and selection. Furthermore, there is a paucity of empirical research that investigates the reliability, validity, and fairness of AI/ML tools in organizational contexts. To stimulate future involvement and research, we share our review and perspective on the current state of AI/ML in talent assessment as well as its benefits and potential pitfalls; and in addressing the issue of fairness, we present experimental evidence regarding the potential for AI/ML to evoke adverse reactions from job applicants during selection procedures. We close by emphasizing increased collaboration among I-O psychologists, computer scientists, legal scholars, and members of other professional disciplines in developing, implementing, and evaluating AI/ML applications in organizational contexts.

show abstract

Section: Organizational Benefits Of Ai/mlmentioning

confidence: 91%

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Gonzalez

Capman²,

Oswald

et al. 2019

PAD

View full text Add to dashboard Cite

show abstract

“…Non-technical individuals as well as technical individuals may benefit from improving Big Data literacy (D'Ignazio and Bhargava, 2015). Big Data in an organisational environment, is based on an organisation to be an 'open-system' and that the data is available from all possible sources (Scholz, 2017). This explains the complexity of Big Data and the choice of theoretical approach which is defined in the following section.…”

Section: Research Contextmentioning

confidence: 99%

Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

Kockum

Dacre

2021

SSRN Journal

View full text Add to dashboard Cite

The era of Big Data has provided business organisations opportunities to improve their management processes. This developmental paper is adopting a mixed-method research approach where qualitative data will underpin a quantitative questionnaire. The early insights are based on an initial eleven qualitative interviews and conceptualised in the following three statements: (i) Project practitioners need to increase their data literacy; (ii) Project practitioners are not utilising the available Big Data based on the 3 Vs; Volume, Velocity and Variety; (iii) Project practitioners need to utilise the structured available data to augment the decision-making process to represent the complex environment of Big Data, the study adopts Complexity Theory as a theoretical framework. When completed, the research will demonstrate the results through System Dynamics modelling.

show abstract

“…New technologies and approaches to put in the picture and demonstrate stories will be needed. For example the photographs, infographics and essays [20].…”

Section: Challenges In Big Datamentioning

confidence: 99%

A Comparative Study on Big Data Applications in Higher Education

Altaye¹

2019

IJETER

View full text Add to dashboard Cite

Analysis of Big data is the new technology for extracting hidden information from the large datasets or data deluge, due to its different properties such as velocity, verities and volume. In this paper we discussed about the overview, existing technologies and tools and issues of big data. In the education field it plays a vital role. When the education system becomes digitized it uses large number of data and also its complex. The motivation of this paper is to express the importance of the applications of big data in education sector by analyzing the past and current situation of big data, finding the different applications related to higher education and to motivate educational institutions to implement the Big data applications in their campus and also encourage the researches to find the solutions for the issues in this field.

show abstract

Big Data in Organizations and the Role of Human Resource Management

Cited by 47 publications

References 392 publications

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

“Where’s the I-O?” Artificial Intelligence and Machine Learning in Talent Management Systems

Project Management Volume, Velocity, Variety: A Big Data Dynamics Approach

A Comparative Study on Big Data Applications in Higher Education

Contact Info

Product

Resources

About