This site is undergoing review. Some annotations were human-generated, some AI-generated — all are being verified.
Back to papersAbstract Summary Main Finding Notes Oxford Research Encyclopedia of Business and Management
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
[Claude classification]: Introduction to Personnel Psychology special issue on AI/ML/Big Data. This is a conceptual/review paper discussing how these methods can advance organizational science, not an empirical study. Reviews four special issue articles (Kumar & Burns 2023 on safety, Song et al 2023 on careers, Sajjadiani et al 2023 on work stress, Min et al 2023 on turnover). Discusses challenges: data accessibility, skill gaps, transparency, privacy, reproducibility, generalizability, interpretability. Oxford Research Encyclopedia reference noted in existing annotations appears incorrect - this is a Personnel Psychology article.
Artificial Intelligence and People at Work
Woolley
2024Personnel Psychology1 citations
Review / survey / meta-analysisInterdisciplinary
AI (General)Machine Learning (pre-LLM)
The transition from manual to service-oriented and information-based work, driven by technological advancements, has reshaped the modern economy, demanding more analytical and cognitive skills. This change challenges traditional management strategies, as knowledge work’s intangibility requires approaches that are the opposite of those that successfully manage manual work. While early artificial intelligence (AI) applications streamlined manual tasks, applying AI to knowledge work revealed complexities in less structured environments. As AI capabilities improve, there is the potential to enhance knowledge-based work by enhancing collective intelligence (CI). At the intersection of management literature and intelligence research are opportunities for AI to improve the three essential functions underlying intelligence in any system—memory, attention, and reasoning. AI augments these functions in human systems, thereby opening the possibility of elevating CI in workplaces. Because of the most pressing research gaps, future exploration is needed in order to understand AI’s role in fostering a collaborative, efficient, and equitable workplace in ways that balance technology optimization with human-centric considerations.
Woo, Tay, and Oswald provide a conceptual framework and review examining how AI, machine learning, and big data techniques can advance organizational research through improved theory development, construct measurement, and prediction, while identifying key practical, ethical, and institutional challenges
AI/ML and big data techniques can advance organizational science by facilitating inductive theorizing, enabling new construct measurement from multimodal data (video, text, sensors), and improving predictive accuracy through cross-validation, though significant challenges remain regarding data access, methodological skills, transparency, and interpretability
Primary Datasets
None (conceptual paper)
Secondary Datasets
None
- Key Methods
- Conceptual review and synthesis of how AI/ML/big data methods can advance organizational research through improved theory development, construct measurement, and prediction
- Sample Period
- Not applicable
- Geographic Coverage
- Not applicable (conceptual paper)
- Sample Size
- Not applicable (conceptual review)
- Level of Analysis
- Individual, Firm, Occupation, Task
- Occupation Classification
- None
- Industry Classification
- None