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Examining the Barriers and Enablers of AI Adoption in a Multimedia Organization

12 pagesPublished: May 26, 2023

Abstract

Decision-making is a complex process that significantly impacts an organization's success. In order to enhance the effectiveness of decision-making, organizations need to consider multiple perspectives, expertise, and experiences. Artificial Intelligence (AI) has received considerable attention from Information Systems (IS) research. Drawing from the General Systems Theory (GST), this qualitative study aims to examine the interplay between AI and decision-making and to identify the barriers and enablers of AI adoption in a South African organization. The data collection was guided by the findings from the literature review followed by a single case study approach using semi-structured interviews as the primary data source. A thematic analysis technique using NVivo software was adopted to facilitate the analysis process by grouping the findings into main themes. This research has identified the following barriers and enablers of AI adoption themes: efficiency, system capability, red tape, business support, job security, staff involvement, and accountability.

Keyphrases: Artificial Intelligence, Barriers and Enablers, decision making, General Systems Theory, South Africa

In: Aurona Gerber and Knut Hinkelmann (editors). Proceedings of Society 5.0 Conference 2023, vol 93, pages 117--128

Links:
BibTeX entry
@inproceedings{Society5.02023:Examining_Barriers_and_Enablers,
  author    = {Thandazile Mkhize and Grant Oosterwyk and Popyeni Kautondokwa},
  title     = {Examining the Barriers and Enablers of AI Adoption in a Multimedia Organization},
  booktitle = {Proceedings of Society 5.0 Conference 2023},
  editor    = {Aurona Gerber and Knut Hinkelmann},
  series    = {EPiC Series in Computing},
  volume    = {93},
  pages     = {117--128},
  year      = {2023},
  publisher = {EasyChair},
  bibsource = {EasyChair, https://easychair.org},
  issn      = {2398-7340},
  url       = {https://easychair.org/publications/paper/mM9R},
  doi       = {10.29007/mgdl}}
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