AI Integration into the Innovation Process: A Study of Hierarchical Perceptions and Management Control Systems in Knowledge-Intensive Firms
(2024) BUSN79 20241Department of Business Administration
- Abstract
- The purpose of this study is to examine the perceptions of various hierarchical levels regarding AI integration into the innovation process within a KIF and examine its influence on the design and usage of MCS. By synthesizing insights into the relationship between AI integration and MCS, this research contributes valuable knowledge to academic discourse and practical implementation strategies, enhancing the understanding of AI's transformative potential in innovation processes.
The study employed a qualitative single-case study approach with an abductive methodology. A structured literature review established the theoretical foundation. Empirical findings are gathered through in-depth semi-structured interviews with KPMG Law employees... (More) - The purpose of this study is to examine the perceptions of various hierarchical levels regarding AI integration into the innovation process within a KIF and examine its influence on the design and usage of MCS. By synthesizing insights into the relationship between AI integration and MCS, this research contributes valuable knowledge to academic discourse and practical implementation strategies, enhancing the understanding of AI's transformative potential in innovation processes.
The study employed a qualitative single-case study approach with an abductive methodology. A structured literature review established the theoretical foundation. Empirical findings are gathered through in-depth semi-structured interviews with KPMG Law employees across hierarchical levels and an external AI expert, ensuring diverse perspectives. Data analysis was conducted using qualitative coding to identify key themes and patterns. These findings were then discussed in relation to the literature to provide comprehensive insights into AI integration in the innovation process and its impact on MCS.
This study employs Haefner et al.'s (2021) framework for AI-driven innovation and Tessier & Otley's (2012) revision of Simons' (1994) Levers of Control framework. It emphasizes the critical role of employee perception in AI integration and examines the impact on MCS within KIFs, focusing on hierarchical perceptions and strategic alignment.
The empirical foundation of this study is a qualitative single case study at KPMG Law. Data was collected through seven in-depth semi-structured interviews, including two individuals per hierarchical position (Partner, Manager, and Staff Level) and an external AI expert, providing comprehensive insights into AI integration and its impact on MCS.
The study found that AI integration in innovation within KIFs currently enhances operational efficiency and decision-making, though used indirectly. However, significant challenges remain. The success relies on a supportive culture, continuous updates, and balanced controls. A potential transition to autonomous AI-driven innovation and direct usage requires overcoming cultural and strategic barriers, ethical considerations, and business model reassessments. Therefore, a phased, strategic approach and adaptable MCS frameworks are essential for leveraging AI's full potential. (Less)
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9155539
- author
- Hüninghake, Christoph LU and Fritzsche, Sebastian LU
- supervisor
-
- Anders Anell LU
- Liesel Klemcke LU
- organization
- course
- BUSN79 20241
- year
- 2024
- type
- H1 - Master's Degree (One Year)
- subject
- keywords
- Artificial Intelligence, Innovation Process, Perception, Management Control, Knowledge-Intensive Firms
- language
- English
- id
- 9155539
- date added to LUP
- 2024-08-07 16:13:56
- date last changed
- 2024-08-07 16:13:56
@misc{9155539,
abstract = {{The purpose of this study is to examine the perceptions of various hierarchical levels regarding AI integration into the innovation process within a KIF and examine its influence on the design and usage of MCS. By synthesizing insights into the relationship between AI integration and MCS, this research contributes valuable knowledge to academic discourse and practical implementation strategies, enhancing the understanding of AI's transformative potential in innovation processes.
The study employed a qualitative single-case study approach with an abductive methodology. A structured literature review established the theoretical foundation. Empirical findings are gathered through in-depth semi-structured interviews with KPMG Law employees across hierarchical levels and an external AI expert, ensuring diverse perspectives. Data analysis was conducted using qualitative coding to identify key themes and patterns. These findings were then discussed in relation to the literature to provide comprehensive insights into AI integration in the innovation process and its impact on MCS.
This study employs Haefner et al.'s (2021) framework for AI-driven innovation and Tessier & Otley's (2012) revision of Simons' (1994) Levers of Control framework. It emphasizes the critical role of employee perception in AI integration and examines the impact on MCS within KIFs, focusing on hierarchical perceptions and strategic alignment.
The empirical foundation of this study is a qualitative single case study at KPMG Law. Data was collected through seven in-depth semi-structured interviews, including two individuals per hierarchical position (Partner, Manager, and Staff Level) and an external AI expert, providing comprehensive insights into AI integration and its impact on MCS.
The study found that AI integration in innovation within KIFs currently enhances operational efficiency and decision-making, though used indirectly. However, significant challenges remain. The success relies on a supportive culture, continuous updates, and balanced controls. A potential transition to autonomous AI-driven innovation and direct usage requires overcoming cultural and strategic barriers, ethical considerations, and business model reassessments. Therefore, a phased, strategic approach and adaptable MCS frameworks are essential for leveraging AI's full potential.}},
author = {{Hüninghake, Christoph and Fritzsche, Sebastian}},
language = {{eng}},
note = {{Student Paper}},
title = {{AI Integration into the Innovation Process: A Study of Hierarchical Perceptions and Management Control Systems in Knowledge-Intensive Firms}},
year = {{2024}},
}