The Feeling Economy: Managing in the Next Generation of Artificial Intelligence (AI)

By Ming-Hui Huang, Roland T. Rust, Vojislav Maksimovic. California Management Review, 2019

462 citations recorded by the source corpus.

The capability of AI is currently expanding beyond mechanical and repetitive to analytical and thinking. A “Feeling Economy” is emerging, in which AI performs many of the analytical and thinking tasks, and human workers gravitate more toward interpersonal and empathetic tasks. Although these people-focused tasks have always been important to jobs, they are now becoming more important to an unprecedented degree. To manage more effectively in the Feeling Economy, managers must adapt the nature of jobs to compensate for the fact that many of the analytical and thinking tasks are increasingly being performed by AI, and, thus, human workers must place increased emphasis on the empathetic and emotional dimensions of their work.

Subfields: Development, Economics and Econometrics, Sociology and Political Science

View Paper: https://www.semanticscholar.org/paper/857c6d87352f40b31a08ca8f5e4ddae6c97aa937

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Citations in the corpus

Rebooting employees: upskilling for artificial intelligence in multinational corporations (2021, background): “As AI deem-phasized mechanical human labor, humans have to upgrade their focus on tasks that are difficult for AI to assume, that is, tasks requiring thinking and feeling skill sets (Huang et al., 2019; Huang & Rust, 2018).”
A systematic literature review on the impact of artificial intelligence on workplace outcomes: A multi-process perspective (2021, unknown): “Antecedents being addressed by studies at the nexus between human resource planning and AI include the improvement of task design (Huang, Rust, & Maksimovic, 2019), the minimization of labor expenses (Atack, Margo, & Rhode, 2019), workers' skills-tasks matching and job execution, (Yoon & Guimaraes,…”
A strategic framework for artificial intelligence in marketing (2020, background): “…addressed some issues, such as managers and management education underpreparing the next generation for feeling and emotional intelligence (Huang and Rust 2018; Huang et al. 2019), and IoT may impose constraints and restrictions on consumer journeys (Hoffman and Novak 2018; Novak and Hoffman 2019).”
Artificial Intelligence and Business Value: a Literature Review (2021, background): “The introduction of AI and displacement or shifting of several conventional job roles is likely to lead to increased tensions, conflict, and feelings of distrust towards the technology itself and the units that promote its deployment (Huang et al., 2019).”
Artificial intelligence in business: State of the art and future research agenda (2020, background): “In fact, some researchers argue that AI will always fail to recognize and use human creativity (e.g., Holford, 2019) and that a new type of Feeling Economy (based on emotions, empathy and interpersonal relations) will drive job creation (Huang, Rust & Maksimovic, 2019).”
The Impact of Artificial Intelligence on Workers' Skills: Upskilling and Reskilling in Organisations (2023, background): “A more recent theory that can be seen as a combination of the different approaches is the “ AI Job Replacement Theory ”, developed by Huang and Rust in 2018.”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “At the personal level, in addition to hope, AI is also creating fear among managers and workers because of the potential job losses (Ransbotham et al., 2018) or “ technological unemployment ” a term that was coined as long ago as 1930 (Jarrahi, 2018).”
Artificial Intelligence and Entrepreneurship: Implications for Venture Creation in the Fourth Industrial Revolution (2020, background): “Scholars draw attention to apparently irreducible and contextualized features of human inter - action and intelligence that cannot be so easily mechanized (Huang et al., 2019 ).”
Engaged to a Robot? The Role of AI in Service (2020, methodology): “There are two defining characteristics of AI that result in AI being able to adapt to changing needs (Huang and Rust 2018; Huang, Rust, and Maksimovic 2019): Self-learning.”