Idea Convergence Quality in Open Innovation Crowdsourcing: A Cognitive Load Perspective

Xusen Cheng, Shixuan Fu, Triparna de Vreede, Gert‐Jan de Vreede, Isabella Seeber, Ronald Maier, Barbara Weber · 2020 · Journal of Management Information Systems

Summary. Open innovation crowdsourcing generates many ideas but struggles to identify quality ones for development. This study tested how different types of cognitive load affect idea convergence quality using laboratory experiments. Germane cognitive load—mental effort directly supporting the task—improved convergence quality and satisfaction, while intrinsic and extraneous cognitive loads reduced satisfaction. Knowledge self-efficacy, goal clarity, and need for cognition strengthened these positive effects, offering practical guidance for designing crowdsourcing tasks.

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@article{cheng-2020-idea-convergence-quality-open-innovation,
  title   = {Idea Convergence Quality in Open Innovation Crowdsourcing: A Cognitive Load Perspective},
  author  = {Xusen Cheng and Shixuan Fu and Triparna de Vreede and Gert‐Jan de Vreede and Isabella Seeber and Ronald Maier and Barbara Weber},
  journal = {Journal of Management Information Systems},
  year    = {2020},
  doi     = {10.1080/07421222.2020.1759344},
  url     = {https://doi.org/10.1080/07421222.2020.1759344}
}

TY  - JOUR
TI  - Idea Convergence Quality in Open Innovation Crowdsourcing: A Cognitive Load Perspective
AU  - Xusen Cheng
AU  - Shixuan Fu
AU  - Triparna de Vreede
AU  - Gert‐Jan de Vreede
AU  - Isabella Seeber
AU  - Ronald Maier
AU  - Barbara Weber
JO  - Journal of Management Information Systems
PY  - 2020
DO  - 10.1080/07421222.2020.1759344
UR  - https://doi.org/10.1080/07421222.2020.1759344
ER  - 

Details

DOI

10.1080/07421222.2020.1759344

Countries

China, United States, Austria, Switzerland

Regions

Asia, North America, Europe

Categories

innovation-theory, innovation-networks, general-innovation

Added

2026-04-28