How and Why a Maximizing Decision-Making Style Affects Individuals’ Financial Well-Being
Date
2025
Authors
Silber, Dietrich
Editors
Advisors
Hoffmann, Arvid
Belli, Alex
Belli, Alex
Journal Title
Journal ISSN
Volume Title
Type:
Thesis
Citation
Statement of Responsibility
Conference Name
Abstract
This thesis contributes to the ongoing discussion in the literature at the intersection of individuals’ decision-making styles, financial well-being, and the integration of digital technologies in financial contexts. Comprising two empirical studies and one methodological paper, the overarching aim of this thesis is to deepen our understanding of maximizing as a decision-making style and its implications for financial well-being, AI adoption intentions, and how to measure it in natural language contexts. The focus of this thesis is on real-world financial decision-making, incorporating both consumer perspectives and novel methodological approaches. The first paper is titled “Maximizing and Financial Well-Being: The Role of Financial Self-Control.” This study investigates the relationship between maximizing and financial well-being through an online survey with 324 participants and an experimental study involving 150 participants. The findings reveal that maximizing, contrary to the common beliefs of previous literature, positively influences financial well-being by enhancing financial self-control. These insights challenge the prevailing view that maximizing is always detrimental to well-being and underscore the importance of understanding its nuanced impact in financial contexts. The second paper, “The Maximizing Decision-Making Style: Embracing AI Advisors in Financial Decisions,” explores the relationship between maximizing and the intention to use AI-based financial advisors. Through a cross-sectional survey of 276 participants and an experimental study with 140 participants, this study demonstrates that maximizers are more likely to use AI advisors, as they align with their preference for thorough evaluations and high decision standards. The findings highlight the mediating roles of perceived algorithm effectiveness and algorithm aversion, contributing to the literature on AI adoption and decision-making styles. The third paper, “A Dictionary-Based Approach for Measuring Maximizing Language Patterns in Natural Language,” introduces a novel methodology for identifying maximizing through linguistic patterns in unstructured text applications. Using a multi-step validation process, the dictionary is applied to two real-world datasets: 182,914 Kickstarter campaign descriptions and 19,748 social media posts from the ten largest U.S. banks. The results confirm the dictionary’s effectiveness in capturing maximizing language patterns across diverse financial contexts, offering an ecologically valid tool for studying maximizing. Overall, the research presented in this thesis advances the understanding of maximizing as a decision-making style and its implications for well-being and AI adoption in the realm of financial decision-making, and measurement of maximizing in textual data. For policymakers, this research highlights the potential of incorporating maximizing principles into financial literacy programs to encourage individuals to make more informed and deliberate financial decisions, enhancing financial well-being. Financial service providers can use these insights to develop tools and resources, such as AI advisors or interactive financial platforms, that align with the preferences of maximizers by supporting thorough evaluations and reducing decision fatigue. Additionally, the dictionary developed in this thesis offers a scalable tool for analyzing maximizing in real-world communication, opening new avenues for research into consumer behavior, personalized financial services, and targeted policy interventions. By integrating maximizing into decision-support systems and educational frameworks, this research emphasizes the importance of balancing decision quality with individual well-being, particularly in increasingly complex financial environments.
School/Discipline
Adelaide Business School
Dissertation Note
Thesis (Ph.D.) -- University of Adelaide, Adelaide Business School, 2025
Provenance
This electronic version is made publicly available by the University of Adelaide in accordance with its open access policy for student theses. Copyright in this thesis remains with the author. This thesis may incorporate third party material which has been used by the author pursuant to Fair Dealing exceptions. If you are the owner of any included third party copyright material you wish to be removed from this electronic version, please complete the take down form located at: http://www.adelaide.edu.au/legals