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Strategic implications of cognitive computing in IS: addressing AI fragmentation through knowledge similarity transformation

Matthias Tuczek et al.

Journal of Strategic Information Systems2025https://doi.org/10.1016/j.jsis.2025.101908article
AJG 4ABDC A*
Weight
0.41

Abstract

• We conduct a review on interdisciplinary cognitive computing systems (CCS) • We introduce CCS as a concept in information systems (IS) research. • We analyze causal models in a graph database for key CCS concepts and relationships. • We develop a novel review approach to knowledge similarity transformation (KST) • We outline strategic implications for value creation and future research directions. Without an integrated model of how the human brain works and processes information, artificial intelligence (AI) will remain a mysterious black box that can misfire as circumstances change. An integrated study of the three cognitive computing components (AI, cognitive psychology, and neurobiology) is necessary to create explainable AI findings. This paper introduces cognitive computing systems (CCS) as a domain for information systems (IS) research. It reviews the interdisciplinary implications of CCS concepts by developing a new computational method, knowledge similarity transformation (KST), to improve digital-augmented literature analysis in fragmented knowledge areas. Based on the dual CCS and KST contribution, this article outlines strategic implications for organizational value creation opportunities and future research directions from a technological, psychological, and physiological perspective.

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https://doi.org/https://doi.org/10.1016/j.jsis.2025.101908

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@article{matthias2025,
  title        = {{Strategic implications of cognitive computing in IS: addressing AI fragmentation through knowledge similarity transformation}},
  author       = {Matthias Tuczek et al.},
  journal      = {Journal of Strategic Information Systems},
  year         = {2025},
  doi          = {https://doi.org/https://doi.org/10.1016/j.jsis.2025.101908},
}

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Strategic implications of cognitive computing in IS: addressing AI fragmentation through knowledge similarity transformation

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Evidence weight

0.41

Balanced mode · F 0.40 / M 0.15 / V 0.05 / R 0.40

F · citation impact0.25 × 0.4 = 0.10
M · momentum0.55 × 0.15 = 0.08
V · venue signal0.50 × 0.05 = 0.03
R · text relevance †0.50 × 0.4 = 0.20

† Text relevance is estimated at 0.50 on the detail page — for your query’s actual relevance score, open this paper from a search result.