Exploring the Relationship between Abusive Management, Self-Efficacy and Organizational Performance in the Context of Human-Machine Interaction Technology and Artificial Intelligence with the Effect of Ergonomics

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dc.authoridLin, Shanyu/0000-0002-3900-0337
dc.authoridSipahi Dongul, Assist.Prof. Esra/0000-0002-6495-4378
dc.authoridHuy, Dinh Tran Ngoc/0000-0002-2358-0699
dc.contributor.authorLin, Shanyu
dc.contributor.authorDongul, Esra Sipahi
dc.contributor.authorUygun, Serdar Vural
dc.contributor.authorOzturk, Mutlu Basaran
dc.contributor.authorHuy, Dinh Tran Ngoc
dc.contributor.authorTuan, Pham Van
dc.date.accessioned2024-11-07T13:35:00Z
dc.date.available2024-11-07T13:35:00Z
dc.date.issued2022
dc.departmentNiğde Ömer Halisdemir Üniversitesi
dc.description.abstract(1) Background: Our study aims to explore the impact of abusive management and self-efficacy on corporate performance in the context of artificial intelligence-based human-machine interaction technology in enterprise performance evaluation. (2) Methods: Surveys were distributed to 578 participants in selected international companies in Turkey, Taiwan, Japan, and China. To reduce uncertainty and errors, the surveys were rigorously evaluated and did not show a normal distribution, as it was determined that 85 participants did not consciously fill out the questionnaires, and the questionnaires from the remaining 493 participants were used. By using the evaluation model of employee satisfaction based on a back propagation (BP) neural network, we explored the manifestation and impact of abusive management and self-efficacy. Using the listed real estate businesses as an example, we proposed a deep learning BP neural network-based employee job satisfaction evaluation model and a human-machine technology-based employee performance evaluation system under situational perception, according to the design requirements of human-machine interaction. (3) Results: The results show that the human-machine interface can log in according to the correct verbal instructions of the employees. In terms of age and education level variables, employees' perceptions of leaders' abusive management and self-efficacy are significantly different from their job performances, respectively (p < 0.01). (4) Conclusions: artificial intelligence (AI)-based human-machine interaction technology, malicious management, and self-efficacy directly affect enterprise performance and employee satisfaction.
dc.identifier.doi10.3390/su14041949
dc.identifier.issn2071-1050
dc.identifier.issue4
dc.identifier.scopus2-s2.0-85124566697
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/su14041949
dc.identifier.urihttps://hdl.handle.net/11480/16260
dc.identifier.volume14
dc.identifier.wosWOS:000768980000001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofSustainability
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_20241106
dc.subjectartificial intelligence
dc.subjectergonomics
dc.subjectsustainable development management
dc.subjecthuman-machine interaction technology
dc.subjectBP neural network
dc.subjectabusive management
dc.subjectenterprise performance
dc.subjecthuman-machine interface performance
dc.titleExploring the Relationship between Abusive Management, Self-Efficacy and Organizational Performance in the Context of Human-Machine Interaction Technology and Artificial Intelligence with the Effect of Ergonomics
dc.typeArticle

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