12 research outputs found

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

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    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

    Data Driven Approach to Multi-Agent Low Level Behavior Generation in Medical Simulations

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    A multi-agent scenario generation framework is designed, implemented and evaluated in the context of a preventive medicine education virtual reality system with data collected from a sensor network at the University of Iowa Hospital. An agent in the framework is a virtual human that represents a healthcare worker. The agent is able to make certain decisions based on the information it gathers from its surroundings in the virtual environment. Distributed sensor networks are becoming very commonplace in public areas for public safety and surveillance purposes. The data collected from these sensors can be visualized in a multi-agent simulation. The various components of the framework include generation of unique agents from the sensor data and low level behaviors such as path determination, directional traffic flows, collision avoidance and overtaking. The framework also includes a facility to prevent foot slippage with detailed animations during the travel period of the agents. Preventive medicine education is the process of educating health care workers about procedures that could mitigate the spread of infections in a hospital. We built an application called the 5 Moments of Hand Hygiene that educates health care workers on the times they are supposed to wash their hands when dealing with a patient. The purpose of the application was to increase the compliance rates of this CDC mandated preventive measure in hospitals across the nation. A user study was performed with 18 nursing students and 5 full-time nurses at the Clemson University School of Nursing to test the usability of the application developed and the realism of the scenario generation framework. The results of the study suggest that the behaviors generated by the framework are realistic and believable enough for use in preventive medicine education applications

    Arabic goal-oriented conversational agent based on pattern matching and knowledge trees

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    Conversational Agents (CA's) are computer agents used in applications to converse with humans using natural language dialogues. They are widely used in different fields like industry, education, marketing, health, and other services. Goal Oriented Conversational Agents (GO-CAs) are agents having a deep strategic purpose which enables them to direct conversations to achieve a certain goal using a specific domain. Typically (CA's) are programmed to have a set of rules that guide the conversation with the user. One technique used to script CA's is through pattern matching algorithms. Such algorithms are used to match the user's dialogue and instigate the conversation through writing a series of scripts that contains the rules and patterns relevant to the domain. Throughout the conversation, values can be extracted from the user's dialogue which allows the CA to respond with the correct answer. CA's have been mainly developed for the English language and very limited work has been carried out in Arabic. This is mainly due to the complexity of the language and the lack of resources supporting the Arabic language. This paper proposes a new CA architecture based on a pattern matching algorithm for the development of a goal orientated Arabic Conversational Agents (ACA). The ACA incorporates a new scripting language and knowledge engineering is used to construct the domain. A prototype ACA was developed and the Iraqi passport system was used as a domain to evaluate the new ACA. The ACA was tested and evaluated by experts within the Iraq Consulate with encouraging results and received positive feedback

    Human-Machine Interfaces for Service Robotics

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    L'abstract è presente nell'allegato / the abstract is in the attachmen

    iRobot : conceptualising SERVBOT for humanoid social robots

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    Services are intangible in nature and, as a result, it is often difficult to measure the quality of the service. The service is usually delivered by a human to a human customer and the service literature shows SERVQUAL can be used to measure the quality of the service. However, the use of social robots during the pandemic is speeding up the process of employing social roots in frontline service settings. An extensive review of the literature shows there is a lack of an empirical model to assess the perceived service quality provided by a social robot. Furthermore, the social robot literature highlights key differences between human service and social robots. For example, scholars have highlighted the importance of entertainment and engagement in the adoption of social robots in the service industry. However, it is unclear whether the SERVQUAL dimensions are appropriate to measure social robots’ service quality. This master’s project will conceptualise the SERVBOT model to assess a social robot’s service quality. It identifies reliability, responsiveness, assurance, empathy, and entertainment as the five dimensions of SERVBOT. Further, the research will investigate how these five factors influence emotional and social engagement and intention to use the social robot in a concierge service setting. To conduct the research, a 2 x 1 (CONTROL vs SERVBOT) x (Concierge) between-subject experiment was undertaken and a total of 232 responses were collected for both stages. The results indicate that entertainment has a positive influence on emotional engagement when service is delivered by a human concierge. Further, assurance had a positive influence on social engagement when a human concierge provided the service. When a social robot concierge delivered the service, empathy and entertainment both influenced emotional engagement, and assurance and entertainment impacted social engagement favourably. For both CONTROL (human concierge) and SERVBOT (social robot concierge), emotional and social engagement had a significant influence on intentions to use. This study is the first to propose the SERVBOT model to measure social robots’ service quality. The model provides a theoretical underpinning on the key service quality dimensions of a social robot and gives scholars and managers a method to track the service quality of a social robot. The study also extends the literature by exploring the key factors that influence the use of social robots (i.e., emotional and social engagement)

    Robust Dialog Management Through A Context-centric Architecture

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    This dissertation presents and evaluates a method of managing spoken dialog interactions with a robust attention to fulfilling the human user’s goals in the presence of speech recognition limitations. Assistive speech-based embodied conversation agents are computer-based entities that interact with humans to help accomplish a certain task or communicate information via spoken input and output. A challenging aspect of this task involves open dialog, where the user is free to converse in an unstructured manner. With this style of input, the machine’s ability to communicate may be hindered by poor reception of utterances, caused by a user’s inadequate command of a language and/or faults in the speech recognition facilities. Since a speech-based input is emphasized, this endeavor involves the fundamental issues associated with natural language processing, automatic speech recognition and dialog system design. Driven by ContextBased Reasoning, the presented dialog manager features a discourse model that implements mixed-initiative conversation with a focus on the user’s assistive needs. The discourse behavior must maintain a sense of generality, where the assistive nature of the system remains constant regardless of its knowledge corpus. The dialog manager was encapsulated into a speech-based embodied conversation agent platform for prototyping and testing purposes. A battery of user trials was performed on this agent to evaluate its performance as a robust, domain-independent, speech-based interaction entity capable of satisfying the needs of its users

    A Case Study on Factors Influencing Retention of Mental Health Clinicians in a New Hampshire Community Mental Health Center

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    This study examined the perspectives of master-level clinical mental health providers and members of leadership at a Community Mental Health Center (CMHC) in New Hampshire, to understand clinician and leadership perspectives as to why master-level providers choose to continue working at CMHCs. Most prior research on turnover in such organizations has focused on why so many leave their positions, however this study instead focuses on factors related to the decision to stay at a specific CMHC in an urban area of New Hampshire. A single case study method was utilized to focus on masters-level mental health care providers with additional interviews with leadership at the CMHC. Some of the findings that will be explored is what draws providers to community mental health centers, the importance of connections with colleagues and leadership, and aspects of why master-level providers stay. The study contributes to the understanding of clinician retention in community mental health centers and provides recommendations for master-level providers, CMHC leadership, and clinical mental health educators. Some of the overarching themes that surface from the data were around why clinicians remain in the CMHC, the reasons why providers do the work they do each day, the draw to CMHC, and reasons why people master-level providers consider leaving a CMHC. Connections with leadership and supervisor were very important in why clinicians want to stay at the CMHC. Licensure contracts were also an area that was explored in this research. Clinicians and members of leadership provided their perspective on licensure contracts and the implementation of the contracts. This dissertation is available in open access at AURA (https://aura.antioch.edu/) and OhioLINK ETD Center (https://etd.ohiolink.edu)
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