Lojistik Hizmet Sağlayıcı İşletmelerde Yapay Zekâ Benimseme Engellerinin Swara Yöntemiyle Önceliklendirilmesi
Prioritizing Artificial Intelligence Adoption Barriers in Logistics Service Provider Firms Using the Swara Method

Summary/Abstract: In the logistics sector, where the pressure of digitalization and automation is increasing, the adoption of artificial intelligence (AI) applications offers significant opportunities, yet it also faces multidimensional barriers. This study focuses on identifying the barriers affecting the use of AI in logistics service provider firms and determining the relative importance of these barriers. The aim of the research is to prioritize seven main and twenty-nine sub-barrier criteria derived from the literature. The data were obtained from the evaluations of 20 experts from the logistics sector and academia through a structured questionnaire and were weighted using the SWARA (Step-wise Weight Assessment Ratio Analysis) method. The findings indicate that among the main barrier groups, human resources and competency-related barriers rank first, followed by technological and infrastructure barriers, and economic barriers in third place. At the sub-criteria level, within the human resources and competency dimension, lack of technical knowledge, insufficient training, and low digital competency stand out, while within the technology and infrastructure dimension, inadequate digital and physical infrastructure emerges as a key issue. Consequently, the study reveals that the primary challenge in AI adoption is not merely technological investment, but also the availability of competent human resources, organizational readiness, and effective resource allocation. Furthermore, the study contributes to the literature by providing a barrier prioritization framework in the logistics context and offers practical implications by guiding investment and transformation priorities.

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