Improving Technology Adoption Process in Accounting and Finance Using Systems Thinking Methods
Name
chun-ayc257-sm-sdm-2024-thesis.pdf
Description
Thesis PDF
Size
7.08 MB
Format
Adobe PDF
Checksum (MD5)
a0c45d50a25228864f831cb63560f23f
Author(s)
Chun, Albert Y.
Advisor(s)
Moser, Bryan R.
Date Issued
September 2024
Publisher
Massachusetts Institute of Technology
Abstract
In the era of digital transformation, Accounting and Finance (A&F) functions face the challenge of making well-informed decisions about which technologies to adopt, which processes to prioritize, and why. These decisions require stakeholders to carefully evaluate available options, assess their implications and tradeoffs, and align diverse preferences to make well-supported investment choices. Conducting this process in a siloed and unstructured manner can lead to inefficiencies.
This study explores the application of Systems Thinking (ST) and Systems Engineering (SE) methods, developing an integrated framework that combines Rich Picture, Object-Process Diagram (OPD), Design Structure Matrix (DSM), and Multi-attribute Tradespace Exploration (MATE) to enhance the technology adoption decision-making process within A&F functions. The focus is on Internal Audit (IA) as a case study for a simplified model and demonstration. While empirical data collection and hypothesis testing were not conducted due to data and time constraints, qualitative insights were gathered from industry practitioners.
Key findings suggest that the integrated framework can potentially reduce the time and effort needed to reach technology adoption decisions. Providing a structured and comprehensive approach ensures that the decision-making process is more holistic, unbiased, and quantifiable. This can also offer post-implementation benefits, as the technologies adopted align better with the organization’s requirements and preferences, resulting in improved efficiency and effectiveness.
This study extends the practical application of ST methodologies into A&F. By presenting this integrated framework, it contributes to the foundation for future research on applying ST to improve the technology adoption decision-making in A&F.
MIT Department
System Design and Management Program.
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