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      Investigating the impact of artificial intelligence on human resource functions in the health sector of China: A mediated moderation model

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          Abstract

          Artificial intelligence (AI) is rapidly transforming the way human resources (HR) functions are carried out in the health sector of China. This study aims to scrutinize the impact of artificial intelligence on the human resource functions operating in the healthcare sector through technological awareness, social media influence, and personal innovativeness. Additionally, this study examines the moderating role of perceived risk between technological awareness and human resources functions. An online questionnaire was administered to human resources professionals in the health sector of China to gather data from 363 respondents. Partial least squares structural equation modeling (PLS-SEM), a statistical procedure, is implemented to investigate the hypothesis of the projected model of artificial intelligence and human resource functions. The research findings reveal that artificial intelligence significantly influences human resource functions through technological awareness, social media influence, and personal innovativeness. Furthermore, perceived risk significantly moderates the relationship between technological awareness and human resource functions. The findings of this study have important implications for HR practitioners and policymakers in the health sectors of China, who can leverage artificial intelligence technologies to optimize and improve organizational performance. However, its adoption needs to be carefully planned and managed to reap the full benefits of this transformative technology.

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          Common method biases in behavioral research: A critical review of the literature and recommended remedies.

          Interest in the problem of method biases has a long history in the behavioral sciences. Despite this, a comprehensive summary of the potential sources of method biases and how to control for them does not exist. Therefore, the purpose of this article is to examine the extent to which method biases influence behavioral research results, identify potential sources of method biases, discuss the cognitive processes through which method biases influence responses to measures, evaluate the many different procedural and statistical techniques that can be used to control method biases, and provide recommendations for how to select appropriate procedural and statistical remedies for different types of research settings.
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            Perceived Usefulness, Perceived Ease of Use, and User Acceptance of Information Technology

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              Structural equation modeling in practice: A review and recommended two-step approach.

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                Author and article information

                Contributors
                Journal
                Heliyon
                Heliyon
                Heliyon
                Elsevier
                2405-8440
                10 November 2023
                November 2023
                10 November 2023
                : 9
                : 11
                : e21818
                Affiliations
                [a ]College of Economics and Management, Beijing University of Technology, Beijing 100124, PR China
                [b ]Institute of Business & Management, University of Engineering and Technology, Lahore 54000, Pakistan
                [c ]College of Environment and Life Science, Beijing University of Technology, Beijing 100124, PR China
                [d ]Department of Mechanical Engineering and Technology, Government College University Faisalabad, Pakistan
                Author notes
                []Corresponding author. xushuo@ 123456bjut.edu.cn
                Article
                S2405-8440(23)09026-6 e21818
                10.1016/j.heliyon.2023.e21818
                10685199
                38034787
                3d107405-d231-455c-b11c-0578650bb9a1
                © 2023 The Authors

                This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/).

                History
                : 20 May 2023
                : 25 October 2023
                : 30 October 2023
                Categories
                Research Article

                artificial intelligence,technological awareness,personal innovativeness,perceived risk,social media influence,human resource functions

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