Optimizing negotiation: 374 108
Quantitative tools to measure efficiency and results
DOI:
https://doi.org/10.51896/rilcods.v7i74.1067Keywords:
negotiation, efficiency evaluation, data envelopment analysis, fuzzy logic, artificial intelligenceAbstract
Evaluating the efficiency of commercial negotiators is key to optimizing results and ensuring successful agreements. Measuring their performance allows you to identify strengths, areas for improvement, and allocate resources strategically. Critical variables to consider include inputs such as time invested, associated costs, and negotiator skills (such as persuasion and adaptability), as well as outputs, which include the number of agreements closed, their economic value, and the level of satisfaction of the parties involved. To quantify these aspects, indicators such as negotiation success rate, time efficiency, return on investment (ROI), and customer perception are used. For a rigorous analysis, methodologies such as Data Envelopment Analysis (DEA) and Fuzzy Logic are used, which are tools that allow for comparing performance among professionals, identifying gaps, and proposing recommendations. Today, Artificial Intelligence (AI) joins the group of tools that not only improve evaluation but also promote training and the development of more effective strategies in commercial negotiation.
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References
Cooper, W. W., Seiford, L. M., & Tone, K. (2007). Data envelopment analysis: A comprehensive text with models, applications, references and DEA-solver software (2nd ed.). Springer.
Dyson, R. G., Allen, R., Camanho, A. S., Podinovski, V. V., Sarrico, C. S., & Shale, E. A. (2001). Pitfalls and protocols in DEA. European Journal of Operational Research, 132(2), 245–259. https://doi.org/10.1016/S0377-2217(00)00149-1.
Figini, S., & Uberti, P. (2009). A statistical framework for measuring the efficiency of negotiations. Journal of Applied Statistics, 36(11), 1243–1258. https://doi.org/10.1080/02664760802661809.
Kersten, G. E., & Noronha, S. J. (1999). WWW-based negotiation support: Design, implementation, and use. Decision Support Systems, 25(2), 135–154. https://doi.org/10.1016/S0167-9236(99)00012-3.
Lewicki, R. J., Saunders, D. M., & Barry, B. (2020). Negociación (7ª ed.). McGraw-Hill Education.
Oosterlaken, I. (2015). Applying value sensitive design (VSD) to AI in negotiation. In Proceedings of the 2015 AAAI Spring Symposium Series. Stanford University, CA.
Yager, R. R. (1988). On ordered weighted averaging aggregation operators in multicriteria decisionmaking. IEEE Transactions on Systems, Man, and Cybernetics, 18(1), 183–190. https://doi.org/10.1109/21.87068.
Yager, R. R., & Kacprzyk, J. (Eds.). (2012). The ordered weighted averaging operators: Theory and applications. Springer Science & Business Media.
Zadeh, L. A. (1965). Fuzzy sets. Information and Control, 8(3), 338–353. https://doi.org/10.1016/S0019-9958(65)90241-X.
Zheng, N., Liu, Z., Ren, P., Ma, Y., Chen, S., Yu, S., Xue, J., Chen, B., & Wang, F. (2017). Hybrid-augmented intelligence: Collaboration and cognition. Frontiers of Information Technology & Electronic Engineering, 18(2), 153–179. https://doi.org/10.1631/FITEE.1700053.
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