| dc.description.abstract | As the capabilities of generative artificial intelligence increases, so does the need for
integration into various industries. The purpose of this study was to explore the drivers,
barriers, and future direction of integration of generative artificial intelligence in a supply chain
industry.
The existing research on generative Artificial Intelligence in the supply chain is very
limited and therefore there are many gaps in existing research. From the drivers that push
integration to the barriers that serve as a hindrance, there is little available data. We also wanted
to understand how this field will develop going forward and the research on this is also limited
so there was a need for exploratory research to help close these gaps. To help with this, valance
theory and dual factor theory was invoked.
The method used in the study was media discourse analysis, by collecting secondary
data theories were made from existing data, while data could come from a large number of
differing opinions, expertise and representatives. The data was found online and consisted of
videos, articles, and blog posts. By utilizing Gioia’s method, the data collected was coded and
used to answer our research questions.
The findings of this study include the drivers, barriers, and future development of the
integration of generative artificial intelligence technology into the supply chain, the findings
reaffirming the drivers, and barriers found in existing research while offering new insights into
the future development as well as drivers and barriers absent from current research.
The study offers both theoretical and practical implications where the research could be
used to help close existing gaps, using media discourse analysis, a method that as far as we can
tell has never been used in a supply chain and generative AI context. Further it can be used as
guides for organizations, regulators, and industry professionals in the supply chain industry. | |