Author
Leon Ciechanowski
Other affiliations: University of Social Sciences and Humanities, University of Warsaw
Bio: Leon Ciechanowski is an academic researcher from Kozminski University. The author has contributed to research in topics: Chatbot & Likert scale. The author has an hindex of 5, co-authored 9 publications receiving 240 citations. Previous affiliations of Leon Ciechanowski include University of Social Sciences and Humanities & University of Warsaw.
Topics: Chatbot, Likert scale, Univariate analysis, Data scraping, Psychology
Papers
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TL;DR: Understanding the user’s side may be crucial for designing better chatbots in the future and, thus, can contribute to advancing the field of human–computer interaction.
283 citations
TL;DR: A novel method of analyzing the content of messages produced in human-chatbot interactions is proposed, using the Condor Tribefinder system the authors developed for text mining that is based on a machine learning classification engine.
131 citations
TL;DR: In this paper, the authors explored synergies between human workers and AI in managerial tasks, and they hypothesized that human-AI collaboration will increase productivity, and the study results generally confirmed their hypothesis.
45 citations
01 Jan 2018
TL;DR: It is found that the usefulness in measuring EEG signal of consumer-grade devices such as Muse is extremely limited in non-laboratory conditions.
Abstract: We have conducted an observational study on persons participating passively in public lectures. During a lecture we were measuring the level of focus of listeners using the Muse EEG-headband as well as conducting an observational study of the usage of the device by experiment participants. The purpose was twofold: to understand to what extent commercially available portable EEG-devices can record synchronicity of experience among the audience and to check what kind of usage participants make of this multi-purpose device. While we got some preliminary insights, we found that the usefulness in measuring EEG signal of consumer-grade devices such as Muse is extremely limited in non-laboratory conditions.
24 citations
17 Jul 2017
TL;DR: The methodology behind the experiment using electromyography as well as other psychophysiological data and a detailed set of questionnaires focused on assessing interactions and willingness to collaborate with a bot are described.
Abstract: Our research is carried out in the context of the ongoing process of introducing artificial intelligence in the area of social interaction with people, with a particular emphasis on interactions in the professional sphere. In this paper, we provide an overview of methods used so far in researching human-bot interaction. We describe the methodology behind our experiment using electromyography as well as other psychophysiological data and a detailed set of questionnaires focused on assessing interactions and willingness to collaborate with a bot. Our purpose is to thoroughly examine the character of the human/non-human interaction process.
19 citations
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TL;DR: This paper aims to provide a survey-based tutorial on potential applications and supporting technologies of Industry 5.0 from the perspective of different industry practitioners and researchers.
314 citations
TL;DR: Analysis of data collected from 370 actual chatbot users reveals that information quality and service quality positively influence consumers’ satisfaction, and that perceived enjoyment, perceived usefulness, and perceived ease of use are significant predictors of continuance intention toward chatbot-based customer service.
210 citations
TL;DR: In this article, the authors studied the role of psychological anthropomorphic characteristics, perceived empathy, and interaction quality in the acceptance of AI devices in the service industry and found that anthropomorphic features alone do not influence acceptance and trust towards AI devices.
159 citations
TL;DR: It is argued that chatbots should be enriched with social characteristics that cohere with users’ expectations, ultimately avoiding frustration and dissatisfaction.
Abstract: Chatbots’ growing popularity has brought new challenges to HCI, having changed the patterns of human interactions with computers. The increasing need to approximate conversational interaction style...
157 citations
TL;DR: A novel method of analyzing the content of messages produced in human-chatbot interactions is proposed, using the Condor Tribefinder system the authors developed for text mining that is based on a machine learning classification engine.
131 citations