Organizing workers and machine learning tools for a less oppressive workplace

Young, Alvin L.; Majchrzak, Ann; Kane, Gerald C.

doi:10.1016/j.ijinfomgt.2021.102353

Cited by 20 publications

(9 citation statements)

References 36 publications

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“…Making organizational structure deployments to maximize the return on investment in AI, companies must consider changing the roles of hybrid team members (Young et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

“…For collaborative work in teams involving robots and humans, it is important how humans and AI are empowered (Schwartz et al, 2016a, b). Business leaders may need to consider new job redesign strategies that take into account changing roles and new skill requirements for human workers to maximize the return on IA investments (Young et al, 2021).But there is a clear lack of theory-based research on how organizations should position the role of AI and develop digital business strategies that incorporate AI (Collins et al, 2021).…”

Section: Theoretical Background 21 Artificial Intelligence In Groupsmentioning

confidence: 99%

“…algorithmic distribution of food delivery) (Coombs, 2020;Thomas, 2020). AI is increasingly involved in organizational operations in various forms, but current research only considers AI as a tool and considers its efficacy (Young et al, 2021). No study has ever considered AI as an individual with similar leaders, colleagues and subordinates.…”

Section: Theoretical Contributionsmentioning

confidence: 99%

“…, 2016a, b). Business leaders may need to consider new job redesign strategies that take into account changing roles and new skill requirements for human workers to maximize the return on IA investments (Young et al. , 2021).…”

Section: Theoretical Backgroundmentioning

confidence: 99%

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Harmony in intelligent hybrid teams: the influence of the intellectual ability of artificial intelligence on human members’ reactions

Chen

Xiang

Wang

et al. 2022

ITP

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PurposeThe purpose of this paper was to investigate the relationships among the intellectual ability of artificial intelligence (AI), cognitive emotional processes and the positive and negative reactions of human members. The authors also examined the moderating role of AI status in teams.Design/methodology/approachThe authors designed an experiment and recruited 120 subjects who were randomly distributed into one of three groups classified by the upper, middle and lower organization levels of AI in the team. The findings in this study were derived from subjects’ self-reports and their performance in the experiment.FindingsRegardless of the position held by AI, human members believed that its intelligence level is positively correlated with dependence behavior. However, when the AI and human members are at the same level, the higher the intelligence of AI, the more likely it is that its direct interaction with team members will lead to conflicts.Research limitations/implicationsThis paper only focuses on human–AI harmony in transactional work in hybrid teams in enterprises. As AI applications permeate, it should be considered whether the findings can be extended to a broader range of AI usage scenarios.Practical implicationsThese results are helpful for understanding how to improve team performance in light of the fact that team members have introduced AI into their enterprises in large quantities.Originality/valueThis study contributes to the literature on how the intelligence level of AI affects the positive and negative behaviors of human members in hybrid teams. The study also innovatively introduces “status” into hybrid organizations.

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“…Making organizational structure deployments to maximize the return on investment in AI, companies must consider changing the roles of hybrid team members (Young et al. , 2021).…”

Section: Discussionmentioning

confidence: 99%

Section: Theoretical Background 21 Artificial Intelligence In Groupsmentioning

confidence: 99%

Section: Theoretical Contributionsmentioning

confidence: 99%

Section: Theoretical Backgroundmentioning

confidence: 99%

See 2 more Smart Citations

Harmony in intelligent hybrid teams: the influence of the intellectual ability of artificial intelligence on human members’ reactions

Chen

Xiang

Wang

et al. 2022

ITP

View full text Add to dashboard Cite

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“…Given the wide range of energy consumption issues, energy simulation software is invariably engaged in the decisionmaking about design strategies and components. The lack of energy reserves on the planet has resulted in the exploration of various methods to reduce and optimize energy consumption [2] . Buildings are one of the largest sources of energy waste, and one of the most vital elements in the construction industry is the virtual modeling of energy consumption in terms of both economics and time.…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis

2022

J Mod Green Energy

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In recent decades, the machine learning theory has been developed in the field of artificial intelligence (AI), as it excludes all shortcomings of manpower, performs complex calculations without rest, and provides prediction benefits for projects. Machine learning models and algorithms extract natural models from the data set, which offers increased problem insight, better decisions, and more accurate predictions. Machine learning has a variety of methods, including supervised, unsupervised, and reinforcement learning, and has been used for building energy modeling in recent years. In this review paper, machine learning in building energy modeling was examined to demonstrate the publications in this area and the relationship between these topics. This paper investigated machine learning methods for building energy modeling using bibliometric analysis and data mining. Therefore, the objective of this research was to give insight into the status of machine learning uses for energy systems in the construction industry. Scientometric software was used for analysis. Deep learning is also a cutting-edge topic of machine learning in 2018 onwards, so a brief explanation in this research was provided to explore a proper connection between machine learning, deep learning, and construction energy modeling.

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Negative Dimensions of Human-Robot and Human-AI Interactions: Frightening Legacies, Emerging Dysfunctions, and Creepiness

Oravec

2022

Social and Cultural Studies of Robots and AI

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Organizing workers and machine learning tools for a less oppressive workplace

Cited by 20 publications

References 36 publications

Harmony in intelligent hybrid teams: the influence of the intellectual ability of artificial intelligence on human members’ reactions

Harmony in intelligent hybrid teams: the influence of the intellectual ability of artificial intelligence on human members’ reactions

Machine Learning Theory in Building Energy Modeling and Optimization: A Bibliometric Analysis

Negative Dimensions of Human-Robot and Human-AI Interactions: Frightening Legacies, Emerging Dysfunctions, and Creepiness

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