Managing Digital Technologies: Evidence from a Field Experiment 3

Last registered on July 08, 2022

View Trial History

Pre-Trial

Trial Information

General Information

Title

Managing Digital Technologies: Evidence from a Field Experiment 3

RCT ID

AEARCTR-0009702

Initial registration date

July 05, 2022

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

July 08, 2022, 9:47 AM EDT

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

Locations

Country

United Kingdom of Great Britain and Northern Ireland

Region

Primary Investigator

Name

Richard Kneller

Affiliation

University of Nottingham

Contact Primary Investigator

Other Primary Investigator(s)

PI Name

Cher Li

PI Affiliation

University of Nottingham

Contact Investigator

PI Name

Anwar Adem

PI Affiliation

University of Nottingham

Contact Investigator

Additional Trial Information

Status

In development

Start date

2022-08-01

End date

2024-03-31

Keywords

Firms & Productivity

Additional Keywords

Web performance, Information Provision, Productivity, UK

JEL code(s)

D22, D24, D83, L25, L66

Secondary IDs

Prior work

This trial does not extend or rely on any prior RCTs.

Abstract

The question how firms improve their productivity is of paramount policy im-
portance for every nation. To harness the benefits from the recent acceleration
of investment in digital technologies, firms must put in place appropriate man-
agement practices. In this paper, we aim to use a field experiment among
UK firms to causally identify the effect of information provision on the perfor-
mance management of a key digital asset - business websites - and the software
technologies used to build them. Specifically, we investigate the importance of
information gap in hindering technology adoption, performance and manage-
ment of digital technologies. For this end, we provide a randomly selected firm
with an information about own website performance, industry common and
best practice together with inputs of digital management and monitoring tools.
After that we assess if these change the adoption of web-based technologies,
and website performance. The information provision or encouragement design
will be carried out to randomly selected firms in the retail industry.

External Link(s)

Registration Citation

Citation

Adem, Anwar , Richard Kneller and Cher Li. 2022. "Managing Digital Technologies: Evidence from a Field Experiment 3." AEA RCT Registry. July 08. https://doi.org/10.1257/rct.9702-1.0

Sponsors & Partners

Experimental Details

Interventions

Intervention(s)

The intervention is an encouragement design, which will provide firms with infor-
mation on their website performance, common and best practice within the indus-
try. The information will be framed to be consistent with best practice performance
management processes for this technology. The information will be sent using mail
and/or email addresses of firms.

Intervention Start Date

2022-08-22

Intervention End Date

2023-08-22

Primary Outcomes

Primary Outcomes (end points)

Our primary outcome will be indicators for website performance

Primary Outcomes (explanation)

collected from Google Lighthouse

Secondary Outcomes

Secondary Outcomes (end points)

Secondary outcomes include the adoption and retention of website softwares

Secondary Outcomes (explanation)

Experimental Design

This is a four-arm block or Stratified randomization design. Within each block,
firms are randomly assigned to one of two arms:
1. Control group [C]
2. Benchmark information treated group[T].

Arm-blocks will based on previous adoption of analytics software in the baseline period, and non-adoption, plus the above/below average performance of website performance measures in the baseline.

Experimental Design Details

Randomization Method

We used Stata’s randtreat command to carry out the randomization

Randomization Unit

Random assignment occurs at the firm-level

Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters

7089 firms

Sample size: planned number of observations

7089 firms

Sample size (or number of clusters) by treatment arms

4492 firms using analytics software in baseline; 2597 firms not using analytics in baseline.
Of the 4492 analytics users in baseline; 2246 above average performance in baseline and 2246 below average. Treatment arms 1123 treated and 1123 control firms for above/below average analytics users in baseline.
Of the 2597 analytics users in baseline; 1298.5 above average performance in baseline and 1298.5 below average. Treatment arms 649 treated and 649 control firms for above/below average analytics non-users in baseline.

Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Not yet available

Supporting Documents and Materials

IRB