Multigenerational Team Experiment

Last registered on November 15, 2024
View Trial History

Pre-Trial

Trial Information

General Information

Title
Multigenerational Team Experiment
RCT ID
AEARCTR-0014772
Initial registration date
November 05, 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
November 15, 2024, 1:29 PM EST

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

Locations

Country
Singapore
Region

Primary Investigator

Name
Zeewan Lee
Affiliation
National University of Singapore
Contact Primary Investigator

Other Primary Investigator(s)

Additional Trial Information

Status
In development
Start date
2025-01-01
End date
2025-03-01
Keywords
Firms & Productivity, Labor
Additional Keywords
JEL code(s)
J24 Human Capital • Skills • Occupational Choice • Labor Productivity
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Singapore is one of the fastest ageing countries while there are impediments to working at old age, stemming from employers’ concerns regarding the employability and productivity of aging workers. Recent literature started to find that age does correspond with workplace wisdom, and older workers may have comparative advantages over younger workers in facilitating collaborative work or mentoring. In this study, we seek to fill the gap in the literature on identifying the specific contributions of older workers in the success of multigenerational team. We evaluate the performances of unigenerational and multigenerational teams through an online experiment simulating real-life teamwork situation in the services industries. Our study is thus expected to generate novel insights regarding older workers’ comparative advantages as well as possible directions of workplace/job reconfiguration for Singapore’s aging workforce.
External Link(s)

Registration Citation

Citation
Lee, Zeewan. 2024. "Multigenerational Team Experiment." AEA RCT Registry. November 15. https://doi.org/10.1257/rct.14772-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
We will randomly assign employees into multigenerational or unigenerational teams. Team composition is thus the intervention.
Intervention (Hidden)
Intervention Start Date
2025-01-01
Intervention End Date
2025-03-01

Primary Outcomes

Primary Outcomes (end points)
Team performance
Primary Outcomes (explanation)
Primary outcomes are team performance measures such as efficiency (time spent on finishing each designed task), cost efficiency, and errors made in the teamwork process.

Secondary Outcomes

Secondary Outcomes (end points)
Soft skills
Secondary Outcomes (explanation)
Secondary outcomes are mediators that we intend to examine, through which team participants make contributions to the overall team performance, such as mentorship, conflict resolution, communication, and technology utilization.

Experimental Design

Experimental Design
We will launch an online RCT study on the reputable online platform (Prolific) to examine and compare the team performances between multigenerational teams and uni-generational teams. By multigenerational team, we mean those composed of both older and younger adults, such as those above 50 years old and below 50 years old. By unigenerational team, we refer to those either composed of all older adults or all younger adults. The study will simulate real-life working conditions in the service industry, more specifically, the retail and delivery industries. Qualified participants will be randomly assigned into either multigenerational or uni-generational teams and finish the same experiments as well as the pre- and post-experiment surveys.
Experimental Design Details
The details of the team-based online experiment are as follows.
a) Pre-Experiment Survey and Initial Setup:
Before starting the experiment, each participant will complete a pre-experiment survey capturing demographic information, preferences regarding teamwork, and expectations for teammates in a collaborative setting. This data will serve as controls in the analysis and enable comparison with post-experiment responses to assess participants' evaluations of both overall team performance and individual teammate contributions.

b) Online Experiment Setup:
Participants will work in pairs to complete two main tasks in the online experiment:

i. Virtual Store Management:
Each team will manage a virtual store, performing two primary tasks:
Identifying discrepancies in inventory stocks and inferring possible causes.
Forecasting future sales trends and planning to restock within a defined budget.

ii. Optimal Delivery Route Planning:
Participants will collaborate to find the best route for delivering 20 packages across Singapore. Provided with an online map, customer requests, delivery addresses, and vehicle options, teams must aim to meet customer requirements while minimizing time, distance, and cost.

c) Teamwork and Communication:
Each team is encouraged to choose a team leader and use the chatbox to discuss strategies, solutions, and timing. To facilitate communication, teams will have access to quick-response bubbles containing phrases like “Good idea!” “I agree.” “I have other opinions…” “Any other thoughts?” “Can you help me?” and “Thank you!” Participants are encouraged to use their judgment and creativity to find a solution that satisfies both team members.

d) Post-Experiment Evaluation:
At the conclusion, participants will complete a post-experiment survey, reflecting on the division of responsibilities and contributions within their team. Their responses will be compared with pre-experiment expectations to analyze shifts in teamwork perceptions.
Randomization Method
Randomization is done by the Prolific in the online experiment
Randomization Unit
Team (two participants per team)
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
NA
Sample size: planned number of observations
700
Sample size (or number of clusters) by treatment arms
NA
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
we plan to have a detectable effect size of 0.3, with a power of 0.8, and a significance level of 0.05
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
Cayuse IRB
IRB Approval Date
2024-01-11
IRB Approval Number
NUS-IRB-2022-752
Analysis Plan

Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials