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Thesis/Dissertation
Date
2025-05
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Business Administration/Finance
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https://doi.org/10.34944/bn8f-fz38
Abstract
Gatekeepers—professional buyers responsible for the selection of investment managers for institutional and wealth management clients—exercise influence over more than $44 trillion in traditional actively managed investments. However, both the investing public and academic research have limited insight into the qualitative framework these investors use to make decisions on their behalf. These studies first explore the qualitative decision-making framework of gatekeepers, both collectively and across two distinct segments—wealth and institutional—shedding light on the criteria and processes that influence the evaluation and selection of investment managers. Secondly, they quantify the relative importance of the identified factors, offering a more precise understanding of their role and providing quantitative validation of their significance. I interviewed 13 gatekeeper manager-leaders, with an average of 17 years of experience, to explore their manager selection criteria and processes. I systematically analyze these data relationships in NVivo using a structured inductive coding process. Secondly, I surveyed 392 participants to collect demographic data and investor choice information. Through Choice-Based Conjoint (CBC) analysis in Sawtooth Lighthouse Studio, I examine how participants make investment decisions based on the identified factors from interviews and use Hierarchical Bayes (HB) estimation to derive utility values and attribute importance scores.
I find that several sub-factors, through theoretical saturation, define the parameters of the gatekeeper decision-making taxonomy, adding depth and meaning to a smaller set of overarching grouping or primary factors. Additionally, I find that manager attractiveness or evaluative value is higher when there is coherence—meaning a general consistency among factors and the ability to validate this consistency across multiple factors. Quantitatively, I find that primary decision factors significantly explain gatekeeper decision-making (RLH = 0.494, with 20 out of 23 part-worth utilities, p < 0.01). Furthermore, I find evidence supporting the existence of synergy between factors identified from interviews (p < 0.001), with a statistically significant 9% improvement in predictive accuracy on holdout tasks, reinforcing the practical and empirical importance of the identified synergy effect.
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