Past as prologue: Taylorism, the new scientific management and managing human capital

Dee Birnbaum, Mark Somers

Research output: Contribution to journalArticlepeer-review

3 Scopus citations

Abstract

Purpose: The purpose of this paper is to explore parallels between scientific management and the new scientific management to gain insight into applications of machine learning and artificial intelligence (AI) to human resource management and employee assessment. Design/methodology/approach: Analysis of Taylor’s work and its interpretation by scholars is contrasted with modern analysis of human resource analytics to demonstrate conceptual and methodological commonalities between the old and the new forms of scientific management. Findings: The analysis demonstrates how the epistemology, ethos and cultural trajectory of scientific management has resulted in a mindset that has influenced the implementation and objectives of the new scientific management with respect to human resources analytics. Social implications: This paper offers an alternative to the view that machine learning and AI as applied to work and employees are beneficial and points out why important challenges have been overlooked and how they can be addressed. Originality/value: Commonalties between Taylorism and the new scientific management have been overlooked so that attempts to gain an understanding of how machine learning is likely to influence work, employees and work organizations are incomplete. This paper provides a new perspective that can be used to address challenges associated with applications of machine learning to work design and employee rights.

Original languageEnglish (US)
Pages (from-to)2610-2622
Number of pages13
JournalInternational Journal of Organizational Analysis
Volume31
Issue number6
DOIs
StatePublished - Nov 7 2023

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Organizational Behavior and Human Resource Management

Keywords

  • Artificial intelligence
  • Human resource analytics
  • Human resource management
  • Machine learning
  • New scientific management
  • Personnel management
  • Scientific management
  • Taylorism

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