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Drivers and Barriers to AI tool adoption in Technical vs. Non-Technical Departments

Stjernfeldt, Timothy LU ; Möttus, David LU and Kemenes Kasza, Atilla LU (2025) FEKH38 20242
Department of Business Administration
Abstract
Thesis Title: Drivers and Barriers to AI tool adoption in Technical vs. Non-Technical Departments
Seminar Date: 16/1 - 2025
Subject/Course: FEKH38, Thesis in Business and Data Analytics, 15 ECTS
Authors: David Möttus, Timothy Stjernfeldt, Attila Kasza
Supervisor: Niklas Lars Hallberg
Key Words: UTAUT, PLS-SEM, AI tools, Performance Expectancy, Facilitating Conditions
Research question: What are the different drivers and barriers that impact the individual adoption of AI tools and how do these differ between individuals who work in technical and non-technical organizational departments?
Purpose: The research aims to explore the factors which hinder or drive AI tool adoption across technical and non technical organizations.
Method:... (More)
Thesis Title: Drivers and Barriers to AI tool adoption in Technical vs. Non-Technical Departments
Seminar Date: 16/1 - 2025
Subject/Course: FEKH38, Thesis in Business and Data Analytics, 15 ECTS
Authors: David Möttus, Timothy Stjernfeldt, Attila Kasza
Supervisor: Niklas Lars Hallberg
Key Words: UTAUT, PLS-SEM, AI tools, Performance Expectancy, Facilitating Conditions
Research question: What are the different drivers and barriers that impact the individual adoption of AI tools and how do these differ between individuals who work in technical and non-technical organizational departments?
Purpose: The research aims to explore the factors which hinder or drive AI tool adoption across technical and non technical organizations.
Method: Data was collected from 95 individuals working across the study population via a survey study. The survey was designed to measure variables such as Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. The collected data was analyzed with a PLS-SEM model.
Theoretical perspectives: The study was based on a modified theoretical framework based on the UTAUT model with elements from the Ecological Framework of Management Decision-Making.
Result: Performance Expectancy and Facilitating Conditions have a statistically significant effect in increasing Use Behaviour among individuals. The effect of Performance Expectancy on Use Behaviour is statistically significantly stronger for individuals in technical departments compared to non technical.
Conclusion: The predictors Performance Expectancy and Facilitating Conditions, in the UTAUT model, have a statistically significant positive impact on Use Behaviour. The effects of Performance Expectancy on Use Behaviour is statistically significantly stronger for individuals working in technical departments. (Less)
Please use this url to cite or link to this publication:
author
Stjernfeldt, Timothy LU ; Möttus, David LU and Kemenes Kasza, Atilla LU
supervisor
organization
course
FEKH38 20242
year
type
M2 - Bachelor Degree
subject
keywords
UTAUT, PLS-SEM, AI tools, Performance Expectancy, Facilitating Conditions
language
English
id
9184190
date added to LUP
2025-02-07 16:43:12
date last changed
2025-02-07 16:43:12
@misc{9184190,
  abstract     = {{Thesis Title: Drivers and Barriers to AI tool adoption in Technical vs. Non-Technical Departments
Seminar Date: 16/1 - 2025
Subject/Course: FEKH38, Thesis in Business and Data Analytics, 15 ECTS
Authors: David Möttus, Timothy Stjernfeldt, Attila Kasza 
Supervisor: Niklas Lars Hallberg
Key Words: UTAUT, PLS-SEM, AI tools, Performance Expectancy, Facilitating Conditions
Research question: What are the different drivers and barriers that impact the individual adoption of AI tools and how do these differ between individuals who work in technical and non-technical organizational departments?
Purpose: The research aims to explore the factors which hinder or drive AI tool adoption across technical and non technical organizations. 
Method: Data was collected from 95 individuals working across the study population via a survey study. The survey was designed to measure variables such as Performance Expectancy, Effort Expectancy, Social Influence and Facilitating Conditions. The collected data was analyzed with a PLS-SEM model.
Theoretical perspectives: The study was based on a modified theoretical framework based on the UTAUT model with elements from the Ecological Framework of Management Decision-Making. 
Result: Performance Expectancy and Facilitating Conditions have a statistically significant effect in increasing Use Behaviour among individuals. The effect of Performance Expectancy on Use Behaviour is statistically significantly stronger for individuals in technical departments compared to non technical. 
Conclusion: The predictors Performance Expectancy and Facilitating Conditions, in the UTAUT model, have a statistically significant positive impact on Use Behaviour. The effects of Performance Expectancy on Use Behaviour is statistically significantly stronger for individuals working in technical departments.}},
  author       = {{Stjernfeldt, Timothy and Möttus, David and Kemenes Kasza, Atilla}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Drivers and Barriers to AI tool adoption in Technical vs. Non-Technical Departments}},
  year         = {{2025}},
}