A central problem in text-based chatbot research revolves around humanness: not only chatbots may reproduce fundamental aspects of being human, but also users may attribute humanlike features to chatbots. Humanness is paramount in human-chatbot conversations because it may profoundly impact the quality of the interaction: for example, when users expect that interactions with chatbots are similar to human conversations and these expectations are not met by the agent, they may easily become frustrated. This is particularly relevant in customer care, where efficient support is essential: here, unmet expectations about the chatbot’s humanlike capabilities can undermine customer satisfaction. In this article, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot in the customer care domain, involving a total of 1,060 conversations. We conducted a single case study to define a preliminary theoretical model of humanness for chatbot technology. The study findings point to a novel conceptualization of humanness in customer-chatbot interaction, highlighting that it is multiple, contextual, modular, and dynamic. Moreover, the theoretical model that we propose explains that the kind of humanness attributed to a chatbot depends on the context in which people expect to interact, the objectives and needs that they aim to fulfill, and the cues that the chatbot exhibits: all these factors may change as the interaction evolves over time, and such changes may further affect the users’ ascriptions of humanness. Finally, we propose design implications of the model, like the need to create “context” and account for the plurality of humanness.

A central problem for chatbots in the customer care domain revolves around how people collaborate with the agent to achieve their own situated goals. The majority of the previous research, however, relied on experiments within artificial settings, rather than on observation of real-world interactions. Moreover, such research mostly analyzed users’ responses to communication breakdowns, rather than the wider collaboration strategies utilized during a conversation. In this paper, we qualitatively analyzed 12,477 real-world exchanges with a task-based chatbot using a Grounded Theory approach as a rigorous coding method to analyze the data. We identified two main aspects of collaboration, behavioral and conversational, and for each aspect we highlighted the different strategies that users perform to “work together” with the agent. These strategies may be utilized from the very beginning of the conversation or in response to misunderstandings in the course of ongoing interactions and may show different evolving dynamics.

In this article, we investigate the case of human-machine dialogues in the specific domain of commercial customer care. We built a corpus of conversations between users and a customer-care chatbot of an Italian Telecom Company, focusing on a sample of conversations where users contact the service asking for explanations about billing issues or overcharges. We observed that users’ requests are often vague, generic or incomprehensible. In such cases, commercial dialogue systems typically ask for clarifications or further details to fully understand users’ specific requests. However, from the corpus analysis it appeared that chatbot's clarifying requests may result in ineffective interactions, with users eventually giving up the conversation or switching to a human agent for a faster query resolution. A recovery strategy is thus needed to anticipate users’ information needs, or intentions. We address this issue resorting to GEN-DS, a dialogue system based on symbolic data-to-text generation. GEN-DS analyzes the user-company contextual relational knowledge, with the aim to generate more relevant answers to unclear questions. In this article, we describe the GEN-DS architecture along with the experiments we carried out to evaluate its output. Results from an offline human evaluation show significant improvements of GEN-DS compared to the original system. These improvements concern properties such as utility, necessity, understandability, and quickness of the information communicated in the dialogue. We believe that GEN-DS techniques may find application in all the dialogue systems that need to manage vague requests and must rely on relational knowledge.

Consumption practices show a strong presence of crystallised social understandings, organising rules and permanent ways of acting that prevent individuals from changing towards more sustainable habits. Over the years, human–computer interaction research tried to help people engage in sustainable lifestyles promoting the health of the Earth. However, by favouring an individualistic and rationalistic approach to design, these attempts often lacked a deep understanding of how individuals are intertwined with social dynamics and organisational structures that might determine their actions. In this article, we aim at exploring novel solutions to support people’s sustainable habits, by focusing on their everyday purchases. Using an ethnographic method grounded in the social practices approach, we analyse the value that individuals ascribe to activities and objects that seem already addressed to sustainable consumption: the packaging-free purchasing practices. Starting from the insights gathered from this research, and leveraging the opportunities opened by the 3D printing technology, we design an interactive system with the aim to break the old buying routines and support the reuse of containers.

This article introduces a framework for creating rich augmented environments based on a social web of intelligent things and people. We target outdoor environments, aiming to transform a region into a smart environment that can share its cultural heritage with people, promoting itself and its special qualities. Using the applications developed in the framework, people can interact with things, listen to the stories that these things tell them, and make their own contributions. The things are intelligent in the sense that they aggregate information provided by users and behave in a socially active way. They can autonomously establish social relationships on the basis of their properties and their interaction with users. Hence when a user gets in touch with a thing, she is also introduced to its social network consisting of other things and of users; she can navigate this network to discover and explore the world around the thing itself. Thus the system supports serendipitous navigation in a network of things and people that evolves according to the behavior of users. An innovative interaction model was defined that allows users to interact with objects in a natural, playful way using smartphones without the need for a specially created infrastructure. The framework was instantiated into a suite of applications called WantEat, in which objects from the domain of tourism and gastronomy (such as cheese wheels or bottles of wine) are taken as testimonials of the cultural roots of a region. WantEat includes an application that allows the definition and registration of things, a mobile application that allows users to interact with things, and an application that supports stakeholders in getting feedback about the things that they have registered in the system. WantEat was developed and tested in a real-world context which involved a region and gastronomy-related items from it (such as products, shops, restaurants, and recipes), through an early evaluation with stakeholders and a final evaluation with hundreds of users.

Wanteat is a framework and a suite of applications which allow users to interact with and explore mixed social networks of smart objects and people in the gastronomy domain, thus promoting the cultural heritage of a territory. Wanteat interaction model is based on the concept of a "wheel" [1].

The Multifunction ISDN Diagnostic Advisor (MIDA) is a PC-based software product providing assistance to Telecom Italia diagnosis operators in ISDN customer complaint management. It supports the dialogue with the customer, by collecting information about faults, and performs a knowledge-based diagnostic process, providing a fast and effective guide to the sequence of operations needed to correctly locate the ISDN trouble (by suggesting network element tests, interpreting data/measures, achieving trouble diagnosis and recommending recovery actions). It also integrates some guides on particular tele-diagnosis commands, on the configuration/diagnostic procedures of the most common clients' terminals and on problems at the customer's premises. An ISDN guide to access technical and commercial information about Telecom Italia ISDN service is also provided within the MIDA environment. Auxiliary features are also included, concerning the possibility to suspend/resume diagnostic sessions, a context-sensitive help for novice users, on-line notebooks, etc.