ORCID Identifier(s)

0000-0002-9910-5341

Graduation Semester and Year

2022

Language

English

Document Type

Dissertation

Degree Name

Doctor of Philosophy in Psychology

Department

Psychology

First Advisor

Amber N Schroeder

Second Advisor

Logan Watts

Abstract

As the completion of short-term tasks on online work platforms such as Amazon Mechanical Turk (MTurk; i.e., eLancing) continues to increase in popularity, it is important to establish an understanding of how traditional work design principles manifest in a digital, transient workforce (Colbert et al., 2016). Therefore, the present effort aimed to do so by empirically testing aspects of Schroeder et al.’s (2021) eLance work design theoretical model through the lens of the Attraction-Selection-Attrition (ASA) model (Schneider, 1987). Data were collected from 325 MTurk workers to examine the mechanisms by which eLancers are attracted to tasks with specific work characteristics, select tasks to design their work, and intend to leave the eLance work role. Results of a path analysis indicated that a number of personality factors (e.g., prosociality) were predictive of increased attraction to specific task characteristics (e.g., task significance). With the exception of feedback from the job, attraction to all task characteristics was predictive of increased selection of tasks with those same characteristics. However, only one selection factor (i.e., feedback from the job) predicted turnover intentions from the eLance role, which was, unexpectedly, a positive effect. No indirect effects emerged for the full mediation model, but two perceptions of work factors (i.e., viewing MTurk work as a calling and a career) were found to interact with specific attraction factors (i.e., task significance and task variety) to predict selection of tasks. These results provide support for some aspects of both the ASA model in this context and Schroeder et al.’s (2021) eLance work design model. The findings of this study have important implications, as they provide information that MTurk requesters can use to attract desirable workers to their tasks (e.g., those high in conscientiousness). The findings are also beneficial to eLancers in that they are provided with increased insight into how to select tasks that align with their personal desires and characteristics.

Keywords

eLance, Work design, Amazon Mechanical Turk, Attraction-Selection-Attrition theory, Task characteristics

Disciplines

Psychology | Social and Behavioral Sciences

Comments

Degree granted by The University of Texas at Arlington

Included in

Psychology Commons

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