research-article

Content Factor Analysis of Crowd Workers' Satisfaction

Authors:

Andrey Barashev,

Guoxin LiAuthors Info & Claims

ICSLT '19: Proceedings of the 5th International Conference on e-Society, e-Learning and e-Technologies

Pages 34 - 38

https://doi.org/10.1145/3312714.3312725

Published: 10 January 2019 Publication History

Abstract

Our study uses semi-structured interviews and Two-factor theory in order to examine drivers influencing task solvers' feeling of satisfaction on the crowdsourcing platform. Satisfaction could effectively motivate workers to exert higher effort and performance thus leading to better working results. Current study results proved that we can delineate platform features that would be attributed to the theoretically proposed content factors of recognition, achievement, responsibility, advancement, personal growth in competence, challenging work, opportunity to do something meaningful, involvement in decision making, and sense of importance to the organization. Besides, this study shows that recognition, personal growth in competence, achievement, and advancement are indeed of the most important for the workers. Hence, improving the above-said features is a good way for an increase in workers' level of satisfaction. A clear understanding of what influence workers could help work providers and platform managers to implement new features and rules so as to facilitate higher satisfaction.

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Cited By

Humayun HMalik MGhazali M(2024)Analysis of Motivational Theories in Crowdsourcing Using Long Tail Theory: A Systematic Literature ReviewInternational Journal of Crowd Science10.26599/IJCS.2023.91000108:1(10-27)Online publication date: Feb-2024
https://doi.org/10.26599/IJCS.2023.9100010

Index Terms

Content Factor Analysis of Crowd Workers' Satisfaction
1. Human-centered computing
  1. Interaction design
    1. Interaction design theory, concepts and paradigms

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cover image ACM Other conferences

ICSLT '19: Proceedings of the 5th International Conference on e-Society, e-Learning and e-Technologies

January 2019

132 pages

ISBN:9781450362351

DOI:10.1145/3312714

Copyright © 2019 ACM.

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Published: 10 January 2019

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the National Natural Science Foundation of China

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ICSLT 2019

ICSLT 2019: 2019 the 5th International Conference on e-Society, e-Learning and e-Technologies

January 10 - 12, 2019

Vienna, Austria

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Cited By

Humayun HMalik MGhazali M(2024)Analysis of Motivational Theories in Crowdsourcing Using Long Tail Theory: A Systematic Literature ReviewInternational Journal of Crowd Science10.26599/IJCS.2023.91000108:1(10-27)Online publication date: Feb-2024
https://doi.org/10.26599/IJCS.2023.9100010

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