Collective intelligence ratio: Measurement of real-time multimodal interactions in team projects
PurposeWith a team interaction analysis model, the authors sought to identify a varying range of individual and collective intellectual behaviors in a series of communicative intents particularly expressed with multimodal interaction methods. In this paper, the authors aim to present a new construct...
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| Main Authors: | , , , , |
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| Format: | text |
| Language: | English |
| Published: |
Institutional Knowledge at Singapore Management University
2011
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| Subjects: | |
| Online Access: | https://ink.library.smu.edu.sg/lkcsb_research/6657 https://ink.library.smu.edu.sg/context/lkcsb_research/article/7656/viewcontent/Collective_IR_pv_2011.pdf |
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| Institution: | Singapore Management University |
| Language: | English |
| Summary: | PurposeWith a team interaction analysis model, the authors sought to identify a varying range of individual and collective intellectual behaviors in a series of communicative intents particularly expressed with multimodal interaction methods. In this paper, the authors aim to present a new construct (i.e. collective intelligence ratio (CIR)) which refers to a numeric indicator representing the degree of intelligence of a team in which each team member demonstrates an individual intelligence ratio (IR) specific to a team goal.Design/methodology/approachThe authors analyzed multimodal team interaction data linked to communicative intents with a Poisson‐hierarchical generalized linear model (HGLM).FindingsThe study found evidence of a distinctive IR for each team member in selecting a communicative method for a certain task, ultimately leading to varying degrees of team CIR.Research limitations/implicationsThe authors limited the type and nature of human intelligence observed with a very short list of categories. Also, the data were evaluated by only one subject matter expert, leading to reliability issues. Therefore, generalization should be limited to situations in which teams, with pre‐specified team goals and tasks, are collaborating in multimodal interaction environments.Practical implicationsThis study presents potential ways to directly or indirectly optimize team performance by identifying and incorporating IRs and CIRs in team composition strategies.Originality/valueIn the literature of team cognition and performance, the authors offer a new insight on team schema by suggesting a new task‐expertise‐person (TEP) unit integrating information on who uses what communicative methods to best tackle on what cognitive task (i.e. optimum cognition with least cognitive burden). Individual and collective intelligence ratios should be considered as new extensions to conventional transactive memory systems in multimodal team interaction scenarios. |
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