Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future

Hila Chalutz Ben Gal

doi:10.1007/978-3-031-24628-9_36

Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future

Hila Chalutz Ben Gal

Afeka Tel Aviv Academic College of Engineering

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

1 Scopus citations

Abstract

The purpose of this chapter is to provide a return on investment (ROI)-based review of human resources-based organizational data mining (HRODM). Organizational data mining (ODM) is defined as leveraging data mining (DM) tools and technologies to enhance organizational decision-making process by transforming data into valuable and actionable knowledge in order to gain a strategic competitive advantage (Nemati, Barko, J Comput Inf Syst 42(4):21–28, 2002; Ind Manag Data Syst 103(4):282–292, 2003). The objectives of this chapter are twofold: First, to offer an integrative analysis of the literature on the topic of HRODM to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic. Second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HRODM by highlighting specific directions as to where the expected ROI may be found. This chapter includes a four-step review and analysis methodology. The chapter provides theoretical and practical information for scholars and professionals aiming to study and adopt HRODM. The ROI-based approach to HRODM presented in this chapter provides a robust tool to compare and contrast different dilemmas and associated values that can be derived from conducting the various types of HRODM projects. A framework is presented that aggregates the findings and clarifies how various HRODM tools influence ROI and how these relationships can be explained. Two examples are presented to demonstrate HRODM implementation.

Original language	American English
Title of host publication	Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition
Subtitle of host publication	Data Mining and Knowledge Discovery Handbook, Third Edition
Publisher	Springer International Publishing
Pages	833-866
Number of pages	34
ISBN (Electronic)	9783031246289
ISBN (Print)	9783031246289
DOIs	https://doi.org/10.1007/978-3-031-24628-9_36
State	Published - Jan 2023
Externally published	Yes

Publication series

Name	Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition

Bibliographical note

Publisher Copyright:
© Springer Nature Switzerland AG 2023.

Access to Document

10.1007/978-3-031-24628-9_36

Cite this

Gal, H. C. B. (2023). Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition: Data Mining and Knowledge Discovery Handbook, Third Edition (pp. 833-866). (Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition). Springer International Publishing. https://doi.org/10.1007/978-3-031-24628-9_36

Gal, Hila Chalutz Ben. / Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future : Themes, Trends, Focus, Future. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition: Data Mining and Knowledge Discovery Handbook, Third Edition. Springer International Publishing, 2023. pp. 833-866 (Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition).

@inbook{7739efd086dd494dbd5952713ad8bf2e,

title = "Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future",

abstract = "The purpose of this chapter is to provide a return on investment (ROI)-based review of human resources-based organizational data mining (HRODM). Organizational data mining (ODM) is defined as leveraging data mining (DM) tools and technologies to enhance organizational decision-making process by transforming data into valuable and actionable knowledge in order to gain a strategic competitive advantage (Nemati, Barko, J Comput Inf Syst 42(4):21–28, 2002; Ind Manag Data Syst 103(4):282–292, 2003). The objectives of this chapter are twofold: First, to offer an integrative analysis of the literature on the topic of HRODM to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic. Second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HRODM by highlighting specific directions as to where the expected ROI may be found. This chapter includes a four-step review and analysis methodology. The chapter provides theoretical and practical information for scholars and professionals aiming to study and adopt HRODM. The ROI-based approach to HRODM presented in this chapter provides a robust tool to compare and contrast different dilemmas and associated values that can be derived from conducting the various types of HRODM projects. A framework is presented that aggregates the findings and clarifies how various HRODM tools influence ROI and how these relationships can be explained. Two examples are presented to demonstrate HRODM implementation.",

author = "Gal, \{Hila Chalutz Ben\}",

note = "Publisher Copyright: {\textcopyright} Springer Nature Switzerland AG 2023.",

year = "2023",

month = jan,

doi = "10.1007/978-3-031-24628-9\_36",

language = "American English",

isbn = "9783031246289",

series = "Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition",

publisher = "Springer International Publishing",

pages = "833--866",

booktitle = "Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition",

}

Gal, HCB 2023, Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future. in Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition: Data Mining and Knowledge Discovery Handbook, Third Edition. Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition, Springer International Publishing, pp. 833-866. https://doi.org/10.1007/978-3-031-24628-9_36

Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future. / Gal, Hila Chalutz Ben.
Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition: Data Mining and Knowledge Discovery Handbook, Third Edition. Springer International Publishing, 2023. p. 833-866 (Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition).

Research output: Chapter in Book/Report/Conference proceeding › Chapter › peer-review

TY - CHAP

T1 - Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future

T2 - Themes, Trends, Focus, Future

AU - Gal, Hila Chalutz Ben

PY - 2023/1

Y1 - 2023/1

N2 - The purpose of this chapter is to provide a return on investment (ROI)-based review of human resources-based organizational data mining (HRODM). Organizational data mining (ODM) is defined as leveraging data mining (DM) tools and technologies to enhance organizational decision-making process by transforming data into valuable and actionable knowledge in order to gain a strategic competitive advantage (Nemati, Barko, J Comput Inf Syst 42(4):21–28, 2002; Ind Manag Data Syst 103(4):282–292, 2003). The objectives of this chapter are twofold: First, to offer an integrative analysis of the literature on the topic of HRODM to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic. Second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HRODM by highlighting specific directions as to where the expected ROI may be found. This chapter includes a four-step review and analysis methodology. The chapter provides theoretical and practical information for scholars and professionals aiming to study and adopt HRODM. The ROI-based approach to HRODM presented in this chapter provides a robust tool to compare and contrast different dilemmas and associated values that can be derived from conducting the various types of HRODM projects. A framework is presented that aggregates the findings and clarifies how various HRODM tools influence ROI and how these relationships can be explained. Two examples are presented to demonstrate HRODM implementation.

AB - The purpose of this chapter is to provide a return on investment (ROI)-based review of human resources-based organizational data mining (HRODM). Organizational data mining (ODM) is defined as leveraging data mining (DM) tools and technologies to enhance organizational decision-making process by transforming data into valuable and actionable knowledge in order to gain a strategic competitive advantage (Nemati, Barko, J Comput Inf Syst 42(4):21–28, 2002; Ind Manag Data Syst 103(4):282–292, 2003). The objectives of this chapter are twofold: First, to offer an integrative analysis of the literature on the topic of HRODM to provide scholars and practitioners a comprehensive yet practical ROI-based view on the topic. Second, to provide practical implementation tools in order to assist decision makers concerning questions of whether and in which format to implement HRODM by highlighting specific directions as to where the expected ROI may be found. This chapter includes a four-step review and analysis methodology. The chapter provides theoretical and practical information for scholars and professionals aiming to study and adopt HRODM. The ROI-based approach to HRODM presented in this chapter provides a robust tool to compare and contrast different dilemmas and associated values that can be derived from conducting the various types of HRODM projects. A framework is presented that aggregates the findings and clarifies how various HRODM tools influence ROI and how these relationships can be explained. Two examples are presented to demonstrate HRODM implementation.

UR - https://www.mendeley.com/catalogue/92650fbc-0032-3e3e-9491-904e0ca6f59d/

U2 - 10.1007/978-3-031-24628-9_36

DO - 10.1007/978-3-031-24628-9_36

M3 - Chapter

SN - 9783031246289

T3 - Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition

SP - 833

EP - 866

BT - Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition

PB - Springer International Publishing

ER -

Gal HCB. Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future. In Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition: Data Mining and Knowledge Discovery Handbook, Third Edition. Springer International Publishing. 2023. p. 833-866. (Machine Learning for Data Science Handbook: Data Mining and Knowledge Discovery Handbook, Third Edition). doi: 10.1007/978-3-031-24628-9_36

Human Resources-Based Organizational Data Mining (HRODM): Themes, Trends, Focus, Future: Themes, Trends, Focus, Future

Abstract

Publication series

Bibliographical note

Access to Document

Other files and links

Fingerprint

Cite this