Yönetim Bilimleri Fakültesi

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Browsing Yönetim Bilimleri Fakültesi by Subject "Artificial Intelligence"

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    Citation - WoS: 25
    Citation - Scopus: 30
    Gigification, Job Engagement and Satisfaction: The Moderating Role of AI Enabled System Automation in Operations Management
    (Taylor & Francis Ltd, 2021) Braganza, Ashley; Chen, Weifeng; Canhoto, Ana; Sap, Serap; 0000-0002-2560-4105; AGÜ, Yönetim Bilimleri Fakültesi, İşletme Bölümü; Sap, Serap; 01. Abdullah Gül University; 03.01. İşletme; 03. Yönetim Bilimleri Fakültesi
    Innovative and highly efficient Artificial Intelligence System Automation (AI-SA) is reshaping jobs and the nature of work throughout supply chain and operations management. It can have one of three effects on existing jobs: no effect, eliminate whole jobs, or eliminate those parts of a job that are automated. This paper focuses on the jobs that remain after the effects of AI-SA, albeit with alterations. We use the term Gigification to describe these jobs, as we posit that the jobs that remain share characteristics of gig work. Our study examines the relationship between Gigification, job engagement and job satisfaction. We develop a theoretical framework to examine the impact of system automation on job satisfaction and job engagement, which we test via 232 survey responses. Our findings show that, while Gigification increases job satisfaction and engagement, AI-SA weakens the positive impact of Gigification on these important worker outcomes. We posit that, over time, the effects of AI-SA on workers is that full-time, permanent jobs will give way to gigified jobs. For future research, we suggest further theory development and testing of the Gigification of operations and supply chain work.
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    Citation - WoS: 52
    Citation - Scopus: 56
    Understanding the Effects of Artificial Intelligence on Energy Transition: The Moderating Role of Paris Agreement
    (Elsevier, 2024) Chishti, Muhammad Zubair; Xia, Xiqiang; Dogan, Eyup; 0000-0003-0476-5177; AGÜ, Yönetim Bilimleri Fakültesi, Ekonomi Bölümü; Dogan, Eyup; 01. Abdullah Gül University; 03.02. Ekonomi; 03. Yönetim Bilimleri Fakültesi
    This study contributes to the existing literature by investigating and confirming a range of diverse outcomes related to the interplay of factors shaping the global energy transition (ET). Employing advanced methodologies, including the extension of the QVAR technique to short-term (SR), medium-term (MR), and long-term (LR) connectedness analysis, as well as the application of the CQ method to explore relationships within varying market conditions and timeframes, the study examines the interconnectedness of critical variables: artificial intelligence (AI), the Belt and Road Initiative (BRI), the Paris Agreement (PA), green technologies (GT), geopolitical risk (GPR), and ET. The findings highlight several crucial insights. Firstly, all selected variables demonstrate substantial interconnectedness across different time horizons, except for MR, which exhibits comparatively weaker connectedness than SR and LR. Secondly, independent series reveal diverse impacts on ET across various market conditions and periods. For example, in SR, most series produce mixed effects on ET, with BRI having primarily adverse consequences and GPR predominantly yielding positive impacts. In MR, the influence of AI, PA, and GT on ET varies, while BRI enhances ET, and GPR essentially hampers it. Notably, in LR, AI, BRI, PA, and GT significantly promote ET, while GPR disrupts its progress. Additionally, the study underscores the dynamic and time-varying nature of the relationships between AI, BRI, PA, GT, GPR, and ET across different market conditions, thus holding essential implications for shaping global policies to foster sustainable energy transitions.