AI Chat Paper
Note: Please note that the following content is generated by AMiner AI. SciOpen does not take any responsibility related to this content.
{{lang === 'zh_CN' ? '文章概述' : 'Summary'}}
{{lang === 'en_US' ? '中' : 'Eng'}}
Chat more with AI
PDF (1.1 MB)
Collect
Submit Manuscript AI Chat Paper
Show Outline
Outline
Show full outline
Hide outline
Outline
Show full outline
Hide outline
Open Access

Analysis of Motivational Theories in Crowdsourcing Using Long Tail Theory: A Systematic Literature Review

Hasan Humayun1,2( )Mohammad Nauman Malik1Masitah Ghazali2
Department of Software Engineering, National University of Modern Languages, Islamabad 44000, Pakistan
Malaysia-Japan International Institute of Technology, Universiti Teknologi Malaysia, Kuala Lumpur 81310, Malaysia
Show Author Information

Abstract

Motivational theories have been extensively studied in a wide range of fields, such as medical sciences, business, management, physiology, sociology, and particularly in the natural sciences. These theories are regarded as crucial in motivating online workers to engage in crowdsourcing. Nevertheless, there is a dearth of research on an overarching review of these theories. We performed a systematic literature review of peer-reviewed published studies focusing on motivational theories to identify popular theories and risks associated with nascent theories presented over the last decade in crowdsourcing. Based on a review of 91 papers from the domain of the natural sciences, we identified 35 motivational theories. The long tail theory helped us to identify the contribution of major influencing theories in a crowdsourcing environment. The results justify the long tail theory based on the Pareto principle of 80/20, which underlines the 20% of the popular motivation theories, namely self-determination, expectancy-value, game, gamification, behavior change, and incentive theory, as a cause of 80%. Similarly, we discussed the risks associated with 10 theories presented over the long tail, which have a frequency equal to 2. Understanding the significant impact, approximately 80%, of widely recognized motivational theories and their role in risk identification is crucial. This understanding can assist researchers in optimizing their results by effectively integrating these theories.

References

【1】
【1】
 
 
International Journal of Crowd Science
Pages 10-27

{{item.num}}

Comments on this article

Go to comment

< Back to all reports

Review Status: {{reviewData.commendedNum}} Commended , {{reviewData.revisionRequiredNum}} Revision Required , {{reviewData.notCommendedNum}} Not Commended Under Peer Review

Review Comment

Close
Close
Cite this article:
Humayun H, Malik MN, Ghazali M. Analysis of Motivational Theories in Crowdsourcing Using Long Tail Theory: A Systematic Literature Review. International Journal of Crowd Science, 2024, 8(1): 10-27. https://doi.org/10.26599/IJCS.2023.9100010

2833

Views

235

Downloads

3

Crossref

4

Scopus

Received: 27 February 2023
Revised: 22 May 2023
Accepted: 29 May 2023
Published: 27 February 2024
© The author(s) 2024.

The articles published in this open access journal are distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/).