Unlocking AI-Based Knowledge Management Potential for SMEs: Exploring Semantic Search Adoption

Timo Grüneke (), Tobias Guggenberger (), Jakob Nusser (), Anna Maria Oberländer (), Jan Stramm () and Alexander Varrentrapp ()
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Timo Grüneke: University of Bayreuth
Tobias Guggenberger: University of Bayreuth
Jakob Nusser: University of Bayreuth
Anna Maria Oberländer: University of Bayreuth
Jan Stramm: University of Bayreuth
Alexander Varrentrapp: University of Bayreuth

A chapter in Artificial Intelligence, Data, and Decision-Making, 2026, pp 169-185 from Springer

Abstract: Abstract Small and medium-sized enterprises (SME) thrive on knowledge-intensive operations, making effective knowledge management critical to their success. Contemporary developments, such as semantic search applications (SSA) leveraging artificial intelligence (AI), promise significant benefits for knowledge management. However, the adoption of such AI-based applications in the context of SME remains still notably limited. Building upon the groundwork laid by previous research on the socio-technical dimensions of AI adoption, we thus investigate the adoption of SSA in a multiple case study within the German manufacturing sector. Hence, contextualizing the adoption of SSA in SMEs using a grounded theoretical framework. Our findings highlight the intricate interplay of organizational readiness, external support, and user satisfaction in facilitating SSA adoption. We believe our framework holds significant potential to guide the adoption of SSA and thus offers valuable insights for navigating the complexities of harnessing the potential of AI-based applications for effective knowledge management in SMEs.

Keywords: Semantic search application adoption; Small and medium-sized enterprises; Knowledge management; Multiple case study (search for similar items in EconPapers)
Date: 2026
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Persistent link: https://EconPapers.repec.org/RePEc:spr:lnichp:978-3-032-08480-4_12

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DOI: 10.1007/978-3-032-08480-4_12

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