Business and pleasure? Relational interaction in conversational UX

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Effects of nonverbal communication on Chatbot's perceived personality and user satisfaction

As artificial intelligence develops rapidly, companies have created exclusive chatbots to facilitate conversational commerce and establish an emotional connection between their brands and their customers. Therefore, shaping the chatbot personality to match the brand image is often the focus of chatbot design. Two studies were conducted to investigate how nonverbal communication elements (avatar, sticker, emoji) affect users' judgment of chatbot personality and explore the effect of chatbot personality on user satisfaction. In Study 1, Kansei engineering was adopted to conduct an online survey using six combinations of nonverbal elements as experimental conditions and the five dimensions of the Brand Personality Scale as Kansei vocabularies. The results revealed that the three nonverbal elements did affect users' perceptions of chatbot personalities; however, the impacts of each element on different personality dimensions varied. In Study 2, based on Study 1, two crowdfunding chatbots with distinct personality traits, sincere and insincere, were developed as the experimental conditions to interact with participants within FB messenger. One hundred fifty valid questionnaires and the click rate of participants during the experiment were collected to measure participants' satisfaction. The results showed that participants were more satisfied with the sincere chatbot than the insincere chatbot. In addition, the personality of the chatbots also affected the participants' judgment of the quality of the messages as well as their willingness to use the chatbots

Persoonallisuus keskustelullisten käyttöliittymien suunnittelussa

Tekoälytutkimuksen viimeaikaiset kehitysaskeleet ovat laajentaneet mahdollisuuksia keskustelullisten käyttöliittymien suhteen. Siinä missä ennen chatbotit nähtiin lähinnä yksinkertaisia tehtäviä tekevinä robotteina, nykyisin ne voidaan kokea ihmismäisinä hahmoina tarjoten potentiaalin esimerkiksi ystävänä toimimisesta. Keskustelulliset käyttöliittymät nähdään yksityisellä sektorilla tulevaisuuden vuorovaikutuskeinona, sillä merkittävällä osalla yrityksistä on chatbot jo osana liiketoimintaansa tai aikomus ottaa sellainen käyttöönsä. Keskustelullisten käyttöliittymien suunnitteluun liittyy haasteita. Ihmismäisen käyttökokemuksen luomiseksi eräs keskeinen suunnittelun kohde on persoonallisuus. Tämän vuoksi tässä tutkielmassa tarkastellaan, millaiset persoonallisuuden piirteet ovat havaittu toimiviksi viimeaikaisessa keskustelullisiin käyttöliittymiin liittyvässä tutkimuksessa sekä millaisilla tavoilla ihmismäistä persoonallisuutta on yleisesti mahdollista simuloida. Jotta persoonallisuuden mallintamista simulaatiota varten voidaan ymmärtää, tarkastellaan myös vallitsevia persoonallisuusteorioita. Toistuvimmat käyttäjien kannalta hyväksi havaitut persoonallisuuden piirteet olivat ystävällisyys, huumorintajuisuus ja samankaltaisuus. Näiden ominaisuuksien huomioiminen on suositeltavaa keskustelullisen käyttöliittymän persoonallisuutta suunniteltaessa

The attitude of Polish young adults to mobile chatbots in e-commerce : selected conditions

The development of technology, including work on artificial intelligence, gives marketers new opportunities regarding communication with customers with the help of chatbots (including e-commerce). The aim of the research was to determine the attitude of Polish young adults to (mobile) chatbots in accordance with the TAM model (intention, attitude, ease and convenience of use) and links with consumer innovation. Statistical analyses (ANOVA and regression analysis) confirmed that innovation measured using the DSI scale (Goldsmith and Hofacker) is related to the attitude to chatbots in the surveyed group. Respondents manifest a sceptical attitude towards this new technology, while having little experience with it

Preconceito linguístico para humanizar as máquinas

Social competence in machines, with the ability to adapt language to the target audience, is still a demand to be explored in the field of artificial intelligence enabling technologies. How can the linguistic description of Brazilian Portuguese that has been developed by Sociolinguistics in Brazil meet a demand for practical application? In this paper, we propose a path to explore how sociolinguistic traits can attribute a human personality to the machine.A competência social nas máquinas, com a capacidade de adaptar a linguagem ao público-alvo, ainda é uma demanda a ser explorada no campo das tecnologias habilitadoras da Inteligência artificial. Como a descrição linguística do português brasileiro que vem sendo desenvolvida pela Sociolinguística no Brasil pode atender a uma demanda de aplicação prática? Neste texto, propomos um caminho para explorar como traços sociolinguísticos podem atribuir uma personalidade humana à máquina

Should Your Chatbot Joke? Driving Conversion Through the Humour of a Chatbot Greeting

Despite the increasing number of companies employing chatbots for tasks that previously needed human involvement, researchers and managers are only now beginning to examine chatbots in customer-brand relationship-building efforts. Not much is known, however, about how managers could modify their chatbot greeting, especially incorporating humour, to increase engagement and foster positive customer–brand interactions. The research aims to investigate how humour in a chatbot welcome message influences customers’ emotional attachment and conversion-to-lead through the mediating role of engagement. The findings of the experiment indicate that conversion-to-lead and emotional attachment rise when chatbots begin with a humorous (vs neutral) greeting. Engagement mediates this effect such that a humorous (vs neutral) greeting sparks engagement and thus makes users more emotionally attached and willing to give out their contact information to the brand. The study contributes to the existing research on chatbots, combining and expanding previous research on human–computer interaction and, more specifically, human–chatbot interaction, as well as the usage of humour in conversational marketing contexts. This study provides managers with insight into how chatbot greetings can engage consumers and convert them into leads

Chatbots with Personality Using Deep Learning

Natural Language Processing (NLP) requires the computational modelling of the complex relationships of the syntax and semantics of a language. While traditional machine learning methods are used to solve NLP problems, they cannot imitate the human ability for language comprehension. With the growth in deep learning, these complexities within NLP are easier to model, and be used to build many computer applications. A particular example of this is a chatbot, where a human user has a conversation with a computer program, that generates responses based on the user’s input. In this project, we study the methods used in building chatbots, what they lack and what can be improved

Comparing How a Chatbot References User Utterances from Previous Chatting Sessions: An Investigation of Users' Privacy Concerns and Perceptions

Chatbots are capable of remembering and referencing previous conversations, but does this enhance user engagement or infringe on privacy? To explore this trade-off, we investigated the format of how a chatbot references previous conversations with a user and its effects on a user's perceptions and privacy concerns. In a three-week longitudinal between-subjects study, 169 participants talked about their dental flossing habits to a chatbot that either, (1-None): did not explicitly reference previous user utterances, (2-Verbatim): referenced previous utterances verbatim, or (3-Paraphrase): used paraphrases to reference previous utterances. Participants perceived Verbatim and Paraphrase chatbots as more intelligent and engaging. However, the Verbatim chatbot also raised privacy concerns with participants. To gain insights as to why people prefer certain conditions or had privacy concerns, we conducted semi-structured interviews with 15 participants. We discuss implications from our findings that can help designers choose an appropriate format to reference previous user utterances and inform in the design of longitudinal dialogue scripting.Comment: 10 pages, 3 figures, to be published in Proceedings of the 11th International Conference on Human-Agent Interaction (ACM HAI'23