WoS İndeksli Yayınlar Koleksiyonu

Permanent URI for this collectionhttps://hdl.handle.net/20.500.12573/394

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    Barriers in Sustainable Lean Supply Chain Management: Implementation in SMEs
    (Ege Univ, Fac. Economics & Admin. Sciences, 2025-02-04) Kazancoglu, Yigit; Takcı, Ebru; Ada, Erhan
    As the world undergoes significant transformations in various domains, including technology, energy supply and communication, the idea of sustainability has become a significant issue. This study investigates the barriers to Sustainable Lean Supply Chain (SLSC) management within Small and Medium-Sized Enterprises (SMEs) and explores the structural interrelationships among these barriers. A comprehensive literature review was carried out to recognize critical elements relevant to the research topic, resulting in the identification of fifteen specific elements that account for 85% of the barriers in SLSC management. The DEMATEL method was used to evaluate the significance and influence levels of these factors. Furthermore, structured in-depth interviews were conducted with ten experts representing sectors that constitute 85% of the SMEs operating in Kayseri Organized Industrial Zone (OIZ), Turkey, including metal products, furniture, plastic packaging, construction materials, textiles and food. The findings reveal that strategies represent the most significant barrier to SLSC management in SMEs. The barriers were analyzed in two dimensions: influencing and influenced factors. The primary influencing factor identified was laws, standards, regulations, and legislation while the most significant influenced factor was found supply and suppliers. The study concludes with findings and actionable recommendations for practitioners and decision-makers.
  • Article
    Citation - WoS: 31
    Citation - Scopus: 37
    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
    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.