Black-Boxes in Executive Assessment

Authors

Sibel OzgenFollow

Document Type

Dissertation

Degree

Doctor of Philosophy (PhD)

Major/Program

Business Administration

First Advisor's Name

Nathan J. Hiller

First Advisor's Committee Title

Committee Chair

Second Advisor's Name

Ravi Gajendran

Second Advisor's Committee Title

Committee Member

Third Advisor's Name

Stav Fainshmidt

Third Advisor's Committee Title

Committee Member

Fourth Advisor's Name

Chockalingam Viswesvaran

Fourth Advisor's Committee Title

Committee Member

Fifth Advisor's Name

Kristin Cullen-Lester

Fifth Advisor's Committee Title

Committee Member

Keywords

Executive selection, promotability, upper echelons, algorithmic decision-making, gender differences, negative signals

Date of Defense

6-23-2022

Abstract

This dissertation examines bias in executive assessment in two studies using field and

experimental data. The first study explores bias in promotability inferences, and the

second examines biases that may emerge in a post-promotion context. The first essay

builds on the gender-based double standards literature. It explores whether the

composition of inputs required to be seen as promotable into the upper echelons

differs for men and women. Based on an analysis of data from 490 focal executives

representing 18 countries, the first essay sheds light on the conditions under which a

gender-based double may be observed in promotability into upper echelon positions.

The second study builds on the first one and seeks to examine whether algorithmicdecision

making can help dismantle biases in organizations. It aims to explore its

downstream consequences for executives who are promoted via algorithmic

determination vs. human decision-making. Building on a robust phenomenon known

as ego-centric advice discounting and research on algorithmic aversion and escalation

bias, it examines supervising executives’ attitudinal and behavioral responses to

algorithmic decision-making in an executive promotion context. In an experimental

study of 680 managers in the U.S, findings highlight the non-financial costs of

algorithmic decision-making faced by algorithm-promoted executives in an executive

promotion context.

Identifier

FIDC010764

Recommended Citation

Ozgen, Sibel, "Black-Boxes in Executive Assessment" (2022). FIU Electronic Theses and Dissertations. 5012.
https://digitalcommons.fiu.edu/etd/5012