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“Interdependent within a Crowd? A Test of Interdependence Theory in Team-Based Microtasking”

Date(s)
December 10, 2025
Location
QBS Student Hub, Entrepreneurial Hub, 01.028
Time
14:00 - 15:00

QUEEN’S BUSINESS SCHOOL INTERNATIONAL BUSINESS, ENTREPRENEURSHIP & MARKETING (IBEM) SEMINAR SERIES

 

Wednesday 10th December

2pm

 

“Interdependent within a Crowd? A Test of Interdependence Theory in Team-Based Microtasking”

 

Andrei Gurca

QUB/QBS

 

Abstract

Drawing on recent innovations within crowd-based microtasking, we test the applicability of extant assumptions on interdependence in the context of emerging forms of digital work. Historically, microtasking featured crowds of anonymous, distributed workers performing simple, repetitive tasks. However, some firms have decided to use teams to enhance the performance of the crowd. Current views of interdependence would assume that the well-defined and simple nature of microtasks precludes any need for interdependence. Based on data from crowdworkers associated with a platform-based, microtask crowdsourcing enterprise, we found evidence that contradicts extant interdependence theory. Specifically, even with consistently low levels of task interdependence, the more crowdworkers held collective, team-based goals the more revenue they generated. Furthermore, the benefits of goal interdependence were enhanced when teams readily shared their knowledge. Thus, although there is an apparent tension between the logics of microtasking (i.e., segmented, simple, and repetitive tasks), and team-based organizing (i.e., encouraging crowdworkers to engage and interact with each other), our findings suggest that teams of distributed crowdworkers can leverage goal and knowledge interdependence to achieve substantial increases in performance, even in the absence of task interdependence. Implications for future research on microtask crowdsourcing and interdependence are presented and discussed.

 

QBS Student Hub, Entrepreneurial Hub, 01.028

Department
Queen's Business School
Audience
All
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