Diversity and Team Performance

Last registered on September 09, 2024
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Pre-Trial

Trial Information

General Information

Title
Diversity and Team Performance
RCT ID
AEARCTR-0014237
Initial registration date
August 21, 2024

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 28, 2024, 3:07 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Last updated
September 09, 2024, 2:46 AM EDT

Last updated is the most recent time when changes to the trial's registration were published.

Locations

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Primary Investigator

Name
Alex Chan
Affiliation
Harvard University
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Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2024-09-10
End date
2025-06-30
Keywords
Behavior, Firms & Productivity, Gender, Labor, Other
Additional Keywords
Team, Diversity
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This research project aims to investigate the impact of team diversity on performance. Previous studies have made use a single task with a single measure of performance. This project seeks to exploit publicly posted data from a series of (upcoming) business competitions spanning multiple continents, where participants are randomly assigned to teams competing on a range of tasks (marketing, accounting, logistics/operations, business intelligence, organizational behavior). Teams can be different by gender composition, as well as information/knowledge that they can access before a task. Teams diverse in different ways can confer performance benefits for some tasks and not others, sometimes even creating a disadvantage for the teams when solving a tasks requiring coordination (Schelling game). Furthermore, team diversity can lead to vastly different performance when the scoring system changes (for the business intelligence task). When teams were given agency over the choice of scoring systems, diverse team elect systems (and hence tasks) that allow for more returns to diversity while non-diverse teams elect systems that yield low/negative returns to diversity.
External Link(s)

Registration Citation

Citation
Chan, Alex. 2024. "Diversity and Team Performance." AEA RCT Registry. September 09. https://doi.org/10.1257/rct.14237-1.4
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Researcher will use publicly posted data for a few upcoming business competitions across countries spanning 4 continents. Participants are randomly assigned to teams competing on a range of tasks (marketing, accounting, logistics/operations, business intelligence, organizational behavior). Teams can be different by gender composition, as well as information/knowledge that they can access before a task.
Intervention Start Date
2024-09-10
Intervention End Date
2025-06-30

Primary Outcomes

Primary Outcomes (end points)
The business competition involves teams of two solving a series of business tasks: accounting, business intelligence, route optimization/logistics, sales, and coordination.

The outcome variable of interest is the performance score for each task. Note that for the business intelligence task, there are a few different scoring systems, scores from each scoring systems will be evaluated as an outcome variable.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The business competition involves teams of two solving a series of business tasks: accounting, business intelligence, route optimization/logistics, sales, and coordination. The accounting task requires real effort to conduct a series of calculations, the performance is measured by number of accounting problems correctly solved. The business intelligence quizzes participants on knowledge about a range of industries (7 industries covered for each quiz), including market sizing and naming major players and trends, and performance is measured by number of correct answers. The route optimization task requires participants to find a cost minimizing path from a starting point to an ending point on a network (represented by a graph where edges are labelled with different costs), performance is measured by cost of the path. The sales task requires the participants to come up with a sale pitch (up to 80 characters) for either a hair product, a dating
App, or a TV series, and the performance is measured by the average rating by a panel of 100 consumers recruited from Prolific. The coordination task is the Schelling game (10 different prompts, where 4 are gendered, 4 are geographical, and 2 are on random
topics), and performance is measured by number of matched responses.

A list of actual questions for one round of the accounting task is attached to this pre-registration.

A list of actual questions for one round of the route optimization task is attached to this pre-registration. In each round, one route optimization would involve the contestant being randomly assigned to only see the left side or the right side while the second route optimization allows the contestant to choose left or right (the team members can choose different sides so that they can collectively see the full map).

The actual prompt for one round of the sales pitch task is attached to this pre-registration. (which product is to be sold will be randomized)

A list of actual questions for one round of the Schelling game is attached to this pre-registration.

The business intelligence quizzes participants on knowledge about a range of industries (7 industries covered for each quiz). A sample of actual questions for one round/quiz is attached to this pre-registration.
Experimental Design Details
Not available
Randomization Method
Participants of the business competition are randomized into teams based on a random number and a random color assigned to them upon arrival at the competition (e.g. someone will get a green card with the number 8). The pairs who received the same number (but different color) will pair up to form a team.
Randomization Unit
Individual
Was the treatment clustered?
Yes

Experiment Characteristics

Sample size: planned number of clusters
300 teams. Because business competition participant numbers are not predictable, we will chronologically include business competition data up until and including the one session that pushed the total teams to over 300 teams (e.g. if the first 5 sessions [starting September 2024] involved a total of 280 teams, and the 6th session included 40 teams, then the data from sessions 1-6 will be included the total number of teams will be 320; and the next two fields should be adjusted accordingly) AND that same session led to all 4 continents being covered (in which case the sample can be larger than 300 teams as well). It will start at South Africa, then Ireland then Hong Kong, then Australia and USA (if by Hong Kong, we already have 300 teams, we still incorporate Australia and then with the first session in the USA).
Sample size: planned number of observations
600 contestants (2* the above)
Sample size (or number of clusters) by treatment arms
150 (or 50%) mixed gender teams (male+female), 75 (or 25%) all female, 75 (or 25%) all male. For industry knowledge and map knowledge, exactly 50% will get diverse information and 50% will get non-diverse information
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials

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IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan