Recognizing and utilizing novel research opportunities with artificial intelligence
Open accessAbstract
As we are witnessing a fundamental transformation of organizations, societies, and economies through the rapid growth of data and development of digital technology (George, Osinga, Lavie, & Scott, 2016), artificial intelligence (AI) has the potential to transform the management field.With the power to automatize, provide predictions of outcomes, and discover patterns in massive amounts of data (Iansiti & Lakhani, 2020), AI changes many aspects of contemporary organizing, including decision-making, problem-solving, and other processes (Bailey, Faraj, Hinds, Leonardi, & von Krogh, 2022).AI also enables firms with capabilities for offering new products and services, developing new business models, and connecting stakeholders.In line with these developments, AI is not only an interesting phenomenon to study in and around organizations (e.g., Krakowski, Luger, & Raisch, 2022;Tang et al., 2022;Tong, Jia, Luo, & Fang, 2021), but also offers management scholars a wealth of research opportunities in enlarging their methodological toolbox to leverage vast amounts and various types of data (e.g., Choudhury, Allen, & Endres, 2020; Vanneste & Gulati, 2022).In our quest to push scientific boundaries, we encourage authors to explore these opportunities within Academy of Management Journal (AMJ).While AI is a trending and "hot" topic discussed by policy-makers, business leaders, and scientists, understanding when and how machines display intelligent behaviors is at the center of modern AI scholarship (Nilsson, 1998).AI represents a broad field of technologies that display intelligent behaviors, including self-awareness, goal formulation, goal-directed action, reasoning, optimization, learning, and autonomous movements.While many readers will immediately recall the Terminator in the movie by the same name, or the rogue artificial psychopath (HAL) in the movie 2001: A Space Odyssey, which epitomizes the horrific efficiency of AI (Clarke, 1968(Clarke, /2000)), computer scientists have long abandoned the idea
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Generative AI systems are not neutral or objective; they depend on the quality of the data used in training and the method of refining or tuning the model, and can be subject to the human biases of their creators.
“Therein lies one of the issues with AI; it is not a neutral and objective 'entity'; it depends on the quality of the data used in training, and the method of refining or tuning the model, which can be subject to the human biases of their creators [7].”
From Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice (2023) · cites this paper as [7]
The paper’s “Challenges of ML Methods” section says ML algorithms can reproduce human biases from training data and model-selection choices. Its “A Call to Action” section also emphasizes representative training data and the influence of bias on parameter choices. These passages support the general point about AI and ML, but do not specifically address generative AI or bias introduced by its creators during model refinement. (LLM, conf=0.84)
Experimental — accuracy not yet measuredAI algorithms can automate business processes, extract knowledge from big data, provide predictions and recommendations, and have superior analytical and computational capabilities compared to human beings.
“This stems from AI algorithms' ability to automate business processes, extract knowledge from big data, provide predictions and recommendations, and superior analytical and computational capabilities compared to human beings (von Krogh, Roberson and Gruber, 2023).”
From Human resource management in the age of generative artificial intelligence: Perspectives and research directions on ChatGPT (2023) · cites this paper as von Krogh, Roberson and Gruber, 2023
The opening passage says AI can automate, predict outcomes, and discover patterns in massive amounts of data. It does not establish that AI provides recommendations or has superior analytical and computational capabilities compared with humans. No page number is available in our copy. (LLM, conf=0.86)
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Flagged first, then by citations
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- Krakowski, S., Luger, J., & Raisch, S. 2022. Artificial intelli- gence and the changing sources of competitive advan- tage. Strategic Management Journal. Forthcoming.
Cited by
Sorted by citations
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Generative Artificial Intelligence: Implications and Considerations for Higher Education Practice
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“Therein lies one of the issues with AI; it is not a neutral and objective ‘entity’; it depends on the quality of the data used in training, and the method of refining or tuning the model, which can be subject to the human biases of their creators [7].”
BackgroundArroyo-Machado, W., Ma, J., Chen, T., Johnson, T. P., Islam, S., Michalegko, L., & Welch, E. (2025) · PLOS One · doi:10.1371/journal.pone.0330416 · Cited by 15 · see citing works
- Authors
- von Krogh, G., Roberson, Q., & Gruber, M.
- Affiliations
- Eidgenössische Technische Hochschule Zürich (ETH Zurich)
- Michigan State University (US)
- École Polytechnique Fédérale de Lausanne (CH)
- Venue
- Academy of Management Journal
- Publisher
- Academy of Management
- Published
- 2023-04
- Volume
- 66
- Issue
- 2
- Pages
- 367-373
- ISSN
- 0001-4273 (ISSN-L)
- 1948-0989
- OpenAlex
- W4366372513
- Cited by
- 164
- References
- 25
- Citations by year
- 2026: 43 · 2025: 64 · 2024: 29 · 2023: 27 · 2022: 1
- FWCI
- 15.37
- Type
- journal article
- Language
- en
- Open access
- bronze
- doi.org
- Locations
- Academy of Management Journal (publishedVersion)
- Infoscience (Ecole Polytechnique Fédérale de Lausanne) (submittedVersion, cc-by-nc-nd)
- Repository for Publications and Research Data (ETH Zurich) (submittedVersion, cc-by-nc-nd)
- ETH Zürich Research Collection
- Topics
- Physical Sciences › Computer Science › Artificial Intelligence › Artificial Intelligence Applications
- Social Sciences › Decision Sciences › Management Science and Operations Research › Impact of AI and Big Data on Business and Society
- Social Sciences › Business, Management and Accounting › Organizational Behavior and Human Resource Management › AI and HR Technologies
- Keywords
- artificial intelligence, artificial intelligence in organizations, machine intelligence, research methodology, big data, decision-making, problem solving, new business models
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