x

We are happy to announce that all trial registrations will now be issued DOIs (digital object identifiers). For more information, see here.
AI, Organizations, and Tacit Knowledge
Last registered on October 16, 2019

Pre-Trial

Trial Information
General Information
Title
AI, Organizations, and Tacit Knowledge
RCT ID
AEARCTR-0004382
Initial registration date
July 29, 2019
Last updated
October 16, 2019 2:36 PM EDT
Location(s)

This section is unavailable to the public. Use the button below to request access to this information.

Request Information
Primary Investigator
Affiliation
Other Primary Investigator(s)
PI Affiliation
Columbia Business School
PI Affiliation
Columbia Business School
Additional Trial Information
Status
In development
Start date
2019-10-16
End date
2020-02-04
Secondary IDs
Abstract
There is a growing interest in understanding the future of work. Given recent advancements in artificial intelligence and machine-learning technology, there are increasing concerns that many of today’s jobs will become automated and performed by machines. Much of the research on the future of work has been grounded in a task-based approach introduced in economics, which focuses on tasks’ technical suitability for automation. While the task-based approach represents an important first step, organizations are key drivers of technological change, and we must understand the role they will play in the future of work. In this paper, we propose one way to think about organizations in this debate. Our approach, grounded in the knowledge-based theory of firm, posits that we can theorize of automation as the transfer of knowledge from an individual to a machine. Such transfer is more difficult when knowledge is tacit, so automation will be more difficult to implement for teams whose coordination is based on organizational routines rather than explicit mechanisms. We test these predictions in a coordination game based in the lab.
External Link(s)
Registration Citation
Citation
Dell'Acqua, Fabrizio, Bruce Kogut and Patryk Perkowski. 2019. "AI, Organizations, and Tacit Knowledge." AEA RCT Registry. October 16. https://doi.org/10.1257/rct.4382-1.1.
Former Citation
Dell'Acqua, Fabrizio, Bruce Kogut and Patryk Perkowski. 2019. "AI, Organizations, and Tacit Knowledge." AEA RCT Registry. October 16. https://www.socialscienceregistry.org/trials/4382/history/55285.
Experimental Details
Interventions
Intervention(s)
We are running experiments in the Behavioral Research Lab at Columbia Business School. Participants will compete in Super Mario Party mini-games that require coordination and communication.
Intervention Start Date
2019-10-16
Intervention End Date
2020-02-04
Primary Outcomes
Primary Outcomes (end points)
Our key primary outcomes are total points scored, total earnings, number of coordination failures, and number of words spoken.
Primary Outcomes (explanation)
These will all be measured directly in the game
Secondary Outcomes
Secondary Outcomes (end points)
n/a
Secondary Outcomes (explanation)
Experimental Design
Experimental Design
Our experiment will follow a 2x3 factorial design. First, teams will be randomly assigned to either the
tacit coordination condition or the explicit coordination condition. Second, teams will be randomly
assigned to an organizational change. A third of teams will have a team-member replaced by an AI, a
third will have a player replaced by a new player (the new hire condition), or a control condition. This
treatment arm will allow us to test whether the effects we observe related to tacit-vs-explicit knowledge
are unique to AI’s or also occur when a new human player is introduced to the team.
Experimental Design Details
Not available
Randomization Method
Players will be randomly assigned to treatment conditions via randomization done in STATA.
Randomization Unit
The randomization unit is the team of four.
Was the treatment clustered?
No
Experiment Characteristics
Sample size: planned number of clusters
6 conditions * 10 teams per condition * 4 players per team = 240 subjects
Sample size: planned number of observations
6 conditions * 10 teams per condition * 4 players per team = 240 subjects
Sample size (or number of clusters) by treatment arms
6 conditions * 10 teams per condition * 4 players per team = 240 subjects
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB
INSTITUTIONAL REVIEW BOARDS (IRBs)
IRB Name
Columbia University
IRB Approval Date
2019-07-15
IRB Approval Number
N/A
Analysis Plan
Analysis Plan Documents
PAP_2019_10_16.pdf

MD5: 6088679f45d31b8e23e6e07ff3dfc104

SHA1: 0e01e2dbda8c845c67ca49c92c75ef4b142296e8

Uploaded At: October 16, 2019