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<p>Wisdom of the crowds (WoC) has been extensively studied as an approach to generate better solutions to solve a wide range of problems, such as financial predictions, marketing, and management decision-making. Nonetheless, there seems to be a lack of clarity about how it works. To better understand how WoC is conceptualized and can be applied, and to create a solid foundation for its development, we have reviewed the WoC and crowd performance literature. Based on our analysis, we: (1) integrate dispersed findings into an Input–Process–Output(IPO)-based multi-stage process model that links preparation inputs to judgment generation and aggregation processes and, ultimately, to the outputs—the manifestation of crowd performance; (2) explain why commonly presumed WoC “enabling” conditions yield mixed effects and translate this logic into three testable propositions: a Goldilocks (non-linear) crowd-size effect, shared biases or common cues as boundary conditions that weaken the benefits of diversity, independence, and decentralization, and a cost-effectiveness trade-off between accuracy and overall value; and (3) conceptualize and define crowd performance and distinguish it from related terms to reduce conceptual ambiguity. The practical implications and directions for future research are also provided.</p>

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Introducing a Wisdom of the Crowd’s Process Model for Achieving Effective Crowd Performance

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年5月25日

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  • Ji Yu,
  • Ali Intezari Harsini,
  • David J. Pauleen,
  • Nazim Taskin,
  • Hamed Jafarzadeh,
  • Shafiq Alam

摘要

Wisdom of the crowds (WoC) has been extensively studied as an approach to generate better solutions to solve a wide range of problems, such as financial predictions, marketing, and management decision-making. Nonetheless, there seems to be a lack of clarity about how it works. To better understand how WoC is conceptualized and can be applied, and to create a solid foundation for its development, we have reviewed the WoC and crowd performance literature. Based on our analysis, we: (1) integrate dispersed findings into an Input–Process–Output(IPO)-based multi-stage process model that links preparation inputs to judgment generation and aggregation processes and, ultimately, to the outputs—the manifestation of crowd performance; (2) explain why commonly presumed WoC “enabling” conditions yield mixed effects and translate this logic into three testable propositions: a Goldilocks (non-linear) crowd-size effect, shared biases or common cues as boundary conditions that weaken the benefits of diversity, independence, and decentralization, and a cost-effectiveness trade-off between accuracy and overall value; and (3) conceptualize and define crowd performance and distinguish it from related terms to reduce conceptual ambiguity. The practical implications and directions for future research are also provided.