In bot we trust: A new methodology of chatbot performance measures

Aleksandra Przegalinska, Leon Ciechanowski, Anna Stroz, Peter Gloor and Grzegorz Mazurek

Business Horizons, 2019, vol. 62, issue 6, 785-797

Abstract: Chatbots are used frequently in business to facilitate various processes, particularly those related to customer service and personalization. In this article, we propose novel methods of tracking human-chatbot interactions and measuring chatbot performance that take into consideration ethical concerns, particularly trust. Our proposed methodology links neuroscientific methods, text mining, and machine learning. We argue that trust is the focal point of successful human-chatbot interaction and assess how trust as a relevant category is being redefined with the advent of deep learning supported chatbots. We propose a novel method of analyzing the content of messages produced in human-chatbot interactions, using the Condor Tribefinder system we developed for text mining that is based on a machine learning classification engine. Our results will help build better social bots for interaction in business or commercial environments.

Keywords: Artificial intelligence; Chatbots; Chatbot performance; Human-computer interaction; Performance goals; Customer trust; Customer experience (search for similar items in EconPapers)
Date: 2019
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Citations: View citations in EconPapers (26)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:bushor:v:62:y:2019:i:6:p:785-797

DOI: 10.1016/j.bushor.2019.08.005

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