Data Sharing Between Firms: Experimental Evidence

Last registered on January 17, 2020
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Pre-Trial

Trial Information

General Information

Title
Data Sharing Between Firms: Experimental Evidence
RCT ID
AEARCTR-0005314
Initial registration date
January 17, 2020

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
January 17, 2020, 11:24 AM EST

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

Locations

Country
Germany
Region

Primary Investigator

Name
Jan Krämer
Affiliation
Universität Passau
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Other Primary Investigator(s)

PI Name
Oliver Zierke
PI Affiliation
University of Passau
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PI Name
Nadine Stüdlein
PI Affiliation
University of Passau
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Additional Trial Information

Status
In development
Start date
2020-01-23
End date
2020-10-30
Keywords
Other
Additional Keywords
data sharing, B2B data markets, laboratory experiment
JEL code(s)
C91, L10, L20
Secondary IDs
Abstract
B2B data markets, such as the ‘Data Intelligence Hub’ by Deutsche Telekom or the ‘Open Data Initiative’ by Adobe, Microsoft, and SAP seek to facilitate the exchange of data between firms. Data sharing is believed to promote firms’ ability to innovate and compete. However, many of these initiatives struggle to get off the ground, in part, because a public goods problem may arise. Thereby firms find it beneficial to use other firms’ data, but are reluctant to share their own data, because they fear that this may give them a relative competitive advantage. However, robust economic insights on how to design B2B data markets in order to facilitate data exchange and trust is scant. To this end, we first develop a simple game-theoretic framework to model the strategic interaction in B2B data markets. Based on this framework, we seek to conduct a series of economic laboratory experiments, in which we systematically vary design parameters of B2B data markets in order to study how they impact firms’ incentives to share data. In particular, we investigate how the dimensions control and transparency affect the willingness to share data. In the control dimension, we consider the extent to which a company can control which other companies receive how much of its data. In the transparency dimension, we consider the extent to which the company is informed about data exchange between other companies.
External Link(s)

Registration Citation

Citation
Krämer, Jan, Nadine Stüdlein and Oliver Zierke. 2020. "Data Sharing Between Firms: Experimental Evidence." AEA RCT Registry. January 17. https://doi.org/10.1257/rct.5314-1.0
Sponsors & Partners
Experimental Details

Interventions

Intervention(s)
We conduct two experimental dimensions in a laboratory environment, the control dimension and the transparency dimension. In both dimensions, we elicit subjects’ incentives to share data, by asking them how much of their data they are willing to share with other firms. In the control dimension, we investigate three different treatments with increasing control over shared data: In the first treatment, each firm can decide how much of its data it wants to share on a platform with all other firms having access to all shared data. Consequently, firms have minimal control over their data. In the second treatment, each firm has additionally the option of excluding individual firms from access to its shared data. Finally, in the third treatment, we consider an environment in which each firm can determine exactly how much of its data it wants to share with each individual firm. Thus, in this treatment each firm has full control over its shared data.
Furthermore, we distinguish two different levels of transparency: First, participants have full information on each individual action, even if they are not involved themselves. Thus, each participant knows exactly which firm has shared how much data with which other firm in previous rounds. However, under incomplete information treatments, each participant only has knowledge on actions with her own involvement. More specifically, in the case of the first control treatment, firms only know how much data the other firms have shared in sum but not individually. In the case of the second and third control treatment, firms only know how much data each firm has shared with them, but not how much data the other firms have shared with each other.
Intervention Start Date
2020-01-23
Intervention End Date
2020-10-30

Primary Outcomes

Primary Outcomes (end points)
Subjects‘ (incentivized) willingness to share data with other firms. In particular, how much data they have shared with each other firm.
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Experiments are run in an experimental laboratory at the School of Business, Economics and Information Systems at the University of Passau. Treatments are randomized at the session level. Participants will be recruited from the student subject pool of the University of Passau. Each subject participates in only one of the six treatments (between-subject-design). In all six treatments, subjects are fully informed about the timeline of the experiment and the consequences of their actions. In the experiment, the participants have to decide with which other firms they want to share how much of its data. Based on the profit the participants achieve during the experiment, the participants receive a monetary compensation in addition to a participation fee. We record a subjects’ willingness to share data with other firms, in particular, how much data they have shared with each other firm, subjects’ answers to an ex-post questionnaire, as well as background statistics and variables related to the data sharing procedure.
Experimental Design Details
Randomization Method
Randomization by computer in office
Randomization Unit
Experimental sessions
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
There are five cohorts of three participants each per experimental session. Observations on the cohort level are assumed to be independent, because subjects do not interact with subjects of other cohorts. Thus, the number of clusters equals the number of observations.
Sample size: planned number of observations
We plan to collect 20 observations per treatment. This corresponds to four experimental sessions per treatment, with 15 participants in a single session, with each session grouped in five cohorts of three participants. Thus, we aim for a total of 360 individual participants across the six treatments.
Sample size (or number of clusters) by treatment arms
CF: 60 participants, CI: 60 participants, EF: 60 participants, EI: 60 participants, IF: 60 participants, II: 60 participants
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2020-01-17
IRB Approval Number
1Flc18WJ
Analysis Plan

Post-Trial

Post Trial Information

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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