Measuring the crowd

Cullina, Eoin; Conboy, Kieran; Morgan, Lorraine

doi:10.1145/2788993.2789841

Cited by 16 publications

(1 citation statement)

References 52 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…How to prepare a high-quality problem statement or RPF (Request for Proposal)? Pollok, Luttgens & Piller (2019b); Gefen et al (2016) Which social media B2B role is the most appropriate for this project (e.g., communication, collaboration, connection, completion, or Cullina, Conboy & Morgan (2015) How will the crowdsourced data be evaluated, assimilated, and disseminated (i.e., absorptive capability to integrate the solution)? Blohm, Leimeister & Krcmar (2013) What will be the level of internal employee participation in knowledge assimilation?…”

Section: Reaching the Participantsmentioning

confidence: 99%

The Power of Many

Edgeman

Sevak

Jensen³

et al. 2021

International Journal of R&D Innovation Strategy

View full text Add to dashboard Cite

Collective efforts of masses provide access to funding and ideas. While such endeavors in a business-to-customer context are well-described, they are less well understood in other contexts such as business-to-business. A literature review that exacts knowledge and inspiration from B2C crowdsourcing and other forms of collective innovation is used. This review generates new knowledge to close this gap and develops a 6-stage innovation framework for Collective Engagement, Intelligence & Innovation (CEI^2) that begins with task specification and concludes with management of inputs generated from the CEI^2 efforts. The framework and the accompanying list of questions may be used by theorists to explore different contexts, and for managers to structure B2B or P2P crowdsourcing more effectively. Contributions of this study include exploration of the theoretical areas of open-source innovation that extend beyond a B2C model, and new ways of effectively structuring CEI^2. Further research may explore the CEI^2 framework through a case study or test it through quantitative study.

show abstract

Section: Reaching the Participantsmentioning

confidence: 99%

The Power of Many

Edgeman

Sevak

Jensen³

et al. 2021

International Journal of R&D Innovation Strategy

View full text Add to dashboard Cite

show abstract

Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

et al. 2023

View full text Add to dashboard Cite

Crowdsourcing holds great potential: macro-task crowdsourcing can, for example, contribute to work addressing climate change. Macro-task crowdsourcing aims to use the wisdom of a crowd to tackle non-trivial tasks such as wicked problems. However, macro-task crowdsourcing is labor-intensive and complex to facilitate, which limits its efficiency, effectiveness, and use. Technological advancements in artificial intelligence (AI) might overcome these limits by supporting the facilitation of crowdsourcing. However, AI’s potential for macro-task crowdsourcing facilitation needs to be better understood for this to happen. Here, we turn to affordance theory to develop this understanding. Affordances help us describe action possibilities that characterize the relationship between the facilitator and AI, within macro-task crowdsourcing. We follow a two-stage, bottom-up approach: The initial development stage is based on a structured analysis of academic literature. The subsequent validation & refinement stage includes two observed macro-task crowdsourcing initiatives and six expert interviews. From our analysis, we derive seven AI affordances that support 17 facilitation activities in macro-task crowdsourcing. We also identify specific manifestations that illustrate the affordances. Our findings increase the scholarly understanding of macro-task crowdsourcing and advance the discourse on facilitation. Further, they help practitioners identify potential ways to integrate AI into crowdsourcing facilitation. These results could improve the efficiency of facilitation activities and the effectiveness of macro-task crowdsourcing.

show abstract

Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing

Gimpel,

Laubacher,

Meindl

et al. 2024

Group Decis Negot

View full text Add to dashboard Cite

Macro-task crowdsourcing presents a promising approach to address wicked problems like climate change by leveraging the collective efforts of a diverse crowd. Such macro-task crowdsourcing requires facilitation. However, in the facilitation process, traditionally aggregating and synthesizing text contributions from the crowd is labor-intensive, demanding expertise and time from facilitators. Recent advancements in large language models (LLMs) have demonstrated human-level performance in natural language processing. This paper proposes an abstract design for an information system, developed through four iterations of a prototype, to support the synthesis process of contributions using LLM-based natural language processing. The prototype demonstrated promising results, enhancing efficiency and effectiveness in synthesis activities for macro-task crowdsourcing facilitation. By streamlining the synthesis process, the proposed system significantly reduces the effort to synthesize content, allowing for stronger integration of synthesized content into the discussions to reach consensus, ideally leading to more meaningful outcomes.

show abstract

Measuring the crowd

Cited by 16 publications

References 52 publications

The Power of Many

The Power of Many

Towards Artificial Intelligence Augmenting Facilitation: AI Affordances in Macro-Task Crowdsourcing

Advancing Content Synthesis in Macro-Task Crowdsourcing Facilitation Leveraging Natural Language Processing

Contact Info

Product

Resources

About