Investigating technological, organizational, and human drivers of AI agent adoption in small business shift scheduling
Tyndyk, Alena (2025)
2025
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Julkaisun pysyvä osoite on
https://urn.fi/URN:NBN:fi:amk-2025112529756
https://urn.fi/URN:NBN:fi:amk-2025112529756
Tiivistelmä
This thesis examines the technological, organizational, and human factors influencing the adoption of AI agents for shift scheduling in a small business. The case company, TaxiMoment, faces operational inefficiencies with manual shift scheduling. The study applies the Technology Acceptance Model (TAM), the Technology-Organization-Environment (TOE) framework, and the Theory of Digital Transformation.
A qualitative study was conducted using semi-structured interviews, an open-ended survey of 23 employees, document analysis, and participant observation. The results reveal cautious optimism among employees regarding AI adoption, including perceived benefits such as time saving and reduced errors. However, adoption depends on transparency, human oversight, and training. Technical readiness is limited by a fragmented data infrastructure, while organizational challenges include informal knowledge management.
The study recommends a phased implementation, employee training, strengthening of the data infrastructure, and human oversight. A simplified AI agent prototype demonstrated technical potential but confirmed the need for iterative implementation. The research provides insights for small businesses on navigating AI adoption and digital transformation.
A qualitative study was conducted using semi-structured interviews, an open-ended survey of 23 employees, document analysis, and participant observation. The results reveal cautious optimism among employees regarding AI adoption, including perceived benefits such as time saving and reduced errors. However, adoption depends on transparency, human oversight, and training. Technical readiness is limited by a fragmented data infrastructure, while organizational challenges include informal knowledge management.
The study recommends a phased implementation, employee training, strengthening of the data infrastructure, and human oversight. A simplified AI agent prototype demonstrated technical potential but confirmed the need for iterative implementation. The research provides insights for small businesses on navigating AI adoption and digital transformation.