Remote Work Communication and Team Performance: The Effects of Flexible Work Schedules

Last registered on January 24, 2022

Pre-Trial

Trial Information

General Information

Title
Remote Work Communication and Team Performance: The Effects of Flexible Work Schedules
RCT ID
AEARCTR-0008806
Initial registration date
January 18, 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
January 24, 2022, 9:38 AM EST

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

Locations

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Primary Investigator

Affiliation
UC San Diego

Other Primary Investigator(s)

PI Affiliation
University of Toronto

Additional Trial Information

Status
In development
Start date
2022-01-31
End date
2022-08-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Effective communication and technological coordination are critical for successful teamwork. Recent innovations in communication technology have changed the landscape of communication methods among team members in the workplace, making virtual communication increasingly easier. One potential advantage of remote work is it supports more work hour flexibility than on-site jobs do, which many workers prefer. Higher levels of job satisfaction are not only associated with greater worker wellbeing, but also with benefits for organizations through higher labor productivity. Moreover, organizations that offer flexible work schedules may be able to attract workers they otherwise could not afford. However, flexible work hours may also hinder effective coordination and communication. Less frequent and lower quality communication may lead to even larger performance losses over time as these teams will be less likely to overcome initial communication barriers. In this project, we will test the trade-offs to flexible and coordinated team work hours using an online natural field experiment conducted among teams of translators working remotely. In particular, we will randomize hired remote translators into teams and randomize whether they are assigned to work specific overlapping hours, or given flexible work schedules. We then re-hire the same teams for a subsequent translation task, and re-randomize them into flexible or coordinated work time schedules. Importantly, our job advertisements include information on whether the task will have flexible work hours or not. This design allows us to test the effect of flex work hours on the quality of applicants and the performance of teams, and whether this varies depending on whether teams have previously worked together.
External Link(s)

Registration Citation

Citation
Hossain, Tanjim and Elizabeth Lyons. 2022. "Remote Work Communication and Team Performance: The Effects of Flexible Work Schedules." AEA RCT Registry. January 24. https://doi.org/10.1257/rct.8806
Experimental Details

Interventions

Intervention(s)
We plan to test our research questions using a field experiment implemented on the global online labor market, UpWork (as in, for instance, Burbano, 2021). We will advertise two job types requiring workers to translate a 2-page text from English to Swahili, and Bengali respectively and hire teams of two to complete each job. Aside from the language the document is to be translated in, the task types are identical.

We have selected a translation task for our research setting for several reasons. First, there is a clear measure of performance on the task; specifically, how many errors the translator made (word error rate), and how readable the translated text is (translation readability scores are summarized in, for instance, Yeung et al, 2018). Second, it requires creativity and problem solving (e.g. Angelone, 2010) and teammates may benefit from being able to effectively communicate. Google translate and other freely available AI translation software are not yet able to optimally translate and we will clarify in the job posting that Google translate has not worked for our purposes. Third, it ensures we will hire teammates who speak similar languages and are, thus, able to communicate. Fourth it is a relatively affordable task on the virtual labor market we are using. Specifically, one page translations like the task we are hiring for cost about $10-$20 to complete on Upwork.

Team members will be asked to complete the task within 24 hours of being hired and receiving the job instructions, which they will receive immediately after being hired. Giving workers 24 hours to complete the job is a norm for short jobs on UpWork, and will give workers enough time to communicate with their teammates even if they are residing in different time zones and/or have different work schedules. Moreover, this will give them plenty of time to complete the task because evidence suggests one page of text takes about 1 hour to translate (e.g. professional translators have an hourly productivity of about 625 words per hour (Haji Sismat, 2016)).

Upon being hired, both workers in each team will be provided the document to be translated and asked to coordinate in order to decide how to work on the task so that each team member has an equal amount of work to do. They will also be provided with a private Slack channel, and instructions on how to download and use Slack, to communicate with their teammates. In order to reduce the incentives for free-riding and to allow us to quantify the extent of free-riding, workers will be asked to highlight the work they did on the final translation.

Job instructions will also inform workers that their manager is planning to hire them for a subsequent translation job in 1-2 months if their teammate is also available to be re-hired. This information is true as we hope to bring all teams back for a second task to investigate whether the value of ease of conversation changes with more mature work teams. Moreover, it provides workers with some incentive to invest in the relationship with their teammate.

Intervention Start Date
2022-01-31
Intervention End Date
2022-06-30

Primary Outcomes

Primary Outcomes (end points)
Outcomes measured both for first and second task:
Completion of task
Translation quality
Communication quantity and quality

Outcomes only measured from first round of hiring:
Quality of job applicants
Primary Outcomes (explanation)
Quality of job applicants - measured by relevance of prior experience and education for task, prior ratings and experience on UpWork
Completion of task - measure of whether a final output was submitted by the team, and whether the submission is fully or partially complete
Translation quality - measured both by comparing submitted output to original document, and by obtaining readability scores from 2 native language speakers per submission
Communication quantity - measured by number of lines typed by each teammate in Slack, and imbalance of lines typed between team members

Secondary Outcomes

Secondary Outcomes (end points)
Willingness to accept second job offer
Survey-based measures of experience working with teammate and on the task
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To causally test our research questions, we will randomly vary the expected flexibility of work schedules in order to test whether schedule flexibility affects job applicant characteristics, ease of coordination, and the quality of output. The specifics of our treatments will be disclosed publicly after the study has been run, and the authors are happy to respond to questions about them via email in the meantime.
Experimental Design Details
Not available
Randomization Method
We will randomize one of our translation languages into one of the two conditions (flexible or coordinated) using a coin flip. The other language will be automatically assigned the opposite condition. Once we have reached the target of 100-110 teams for a given language under that condition, we will run the opposite condition for that language.

We will re-randomize all teams into the coordination and flexible conditions for the second round using Stata's generate uniform distribution number assignment command (gen uniform()). This randomization will be stratified by the first period treatment assignment.
Randomization Unit
We are randomizing at the level of the translation language in the first period. In particular, one language will start with flexible job posts, and the other will start with coordinated job posts, and switch the former to coordinated and the latter to flexible once the target sample size for the first assigned treatments have been reached. We are not randomizing treatments by language across days because it might seem strange to job applicants of a given job that we are changing our job postings daily. This will mean that some job will finish the first treatment before the other. We will wait for both languages to be done with the first treatment before starting the other treatment.

In the second round, we will randomize at the level of the team.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
410-420 teams of two works (820-840 workers in total)
Sample size: planned number of observations
400-440 teams
Sample size (or number of clusters) by treatment arms
200-220 teams per treatment arm, or 100-110 teams per language treatment arm
100-110 teams, Bengali translation, coordinated condition
100-110 teams, Bengali translation, flexible condition
100-110 teams, Swahili translation, coordinated condition
100-110 teams, Swahili translation, flexible condition
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
University of California San Diego Human Research Protections Program
IRB Approval Date
2021-05-20
IRB Approval Number
210574
IRB Name
University of Toronto Research Ethics Board
IRB Approval Date
2021-08-11
IRB Approval Number
41413