Abstract
In this paper we discuss the relationships among negotiations, integrative and distributive speech acts, and classification of negotiation outcome. Our findings present how using automated linguistic analysis can show the trajectory of negotiations towards convergence (resolution) or divergence (non-resolution) and how these trajectories accurately classify negotiation outcomes. Consequently, we present the results of our negotiation outcome classification study, in which we use a corpus of 20 transcripts of actual face-to-face negotiations to build and test two classification models. The first model uses language features and speech acts to place negotiation utterances onto an integrative and distributive scale. The second uses that scale to classify the negotiations themselves as successful or unsuccessful at the midpoint, three-quarters of the way through, and at the end of the negotiation. Classification accuracy rates were 80, 75, and 85 % respectively.
Original language | English (US) |
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Pages (from-to) | 135-151 |
Number of pages | 17 |
Journal | Group Decision and Negotiation |
Volume | 22 |
Issue number | 1 |
DOIs | |
State | Published - Jan 2013 |
Keywords
- Classification
- Language features
- Machine learning
- Negotiation
- Negotiation outcome
- Speech acts
ASJC Scopus subject areas
- General Decision Sciences
- Arts and Humanities (miscellaneous)
- General Social Sciences
- Strategy and Management
- Management of Technology and Innovation