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Number of publications over time (n = 107, included in analysis of findings)

Number of publications over time (n = 107, included in analysis of findings)

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Article
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Intelligent agents (IAs) are permeating both business and society. However, interacting with IAs poses challenges moving beyond technological limitations towards the human-computer interface. Thus, the knowledgebase related to interaction with IAs has grown exponentially but remains segregated and impedes the advancement of the field. Therefore, we...

Citations

... Conceptual parallels could therefore be drawn between the constantly evolving functionality of an AI-based application and the timeline feature of certain social media applications offering a near-infinite source of novelty to drive compulsive and, or addicted use (as extreme forms of continued use) [29]. Non-AI-based [30] Empirical study Initial use Ensemble [31][32][33] Empirical study Initial use Proxy [34] Literature review Initial use Proxy [35,36] Empirical study Continued use Ensemble [37,38] Empirical study Continued use Proxy [39] Literature review Continued use Proxy AI-based [40,41] Literature review n/a Computational [42,43] Conceptual study n/a Proxy [44][45][46][47] Empirical study Initial use Proxy [48] Literature review Initial use Proxy [49] Empirical study Initial use Proxy/Comp. [50] Empirical study Initial use Proxy [51] Empirical study Continued use Proxy Similarly, with non-AI-based applications records, the majority of screened publications adopted a proxy view of the AI-based applications, whereas the remainder of the records adopted a computational view, thus concentrating expressly on the "capabilities of the technology to represent, manipulate, store, retrieve, and transmit information" [25, p. 127]. ...
Chapter
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This research in progress manuscript reports on the preliminary findings of a scoping literature review that uncovers the constituent elements of Artificial Intelligence-based (AI) applications as a radically new type of digital artifact and compares them with those of non-AI-based applications to articulate theoretical propositions related to their use within organizations. A preliminary screening of a random sample of 10 non-AI based and 11 AI-based application-related records was conducted to compare and contrast the focus and perspectives adopted by extant research. The findings of this initial screening indicate a tendency towards viewing AI-based applications as black boxes. These findings further suggest that the study of continued use of these applications may be a potentially rich area for future empirical research as most screened records focused on their initial use. The next steps of this review, which will include, among others, a narrative, thematic analysis, and the identification of gaps within extant research, may confirm or broaden these conclusions.
... IA design relies heavily on autonomous systems principles [2] or, more recently, on developmental psychology guidelines [3] with additional considerations such as user acceptance [4] having been recently factored in. In order to carry out their tasks efficiently, IAs frequently rely on ML such as case base reasoning [5] or other scehemes to deal with uncertain or evolving digital environments [6] involving multiple users or agents [7]. ...
Technical Report
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Citation graph mining is an indispensable tool in scientometrics since such graphs succinctly represent researcher collaborations and their respective evolution over time. This allows the discovery of scientific communities and workgroups as well as the the recommendation of potential coworkers. The latter in turn is critical in the flow and formulation of new ideas, either in human led or algorithmically driven efforts. Large citation graphs can be efficiently mined for community structure potential in the form of latent links with high probability of existence by intelligent agents employing a wide array of machine learning techniques, including random forests and neural networks. As a concrete example, the proposed intelligent agent architecture has been tested using a popular bibliometric dataset as a benchmark and a plethora of structural attributes such as the Adamic-Adar score and preferential attachment. The citation graph, containing highly imbalanced numbers of positive and negative examples, was stored in an standalone Neo4j instance, whereas the intelligent agents, implemented in Python, communicated with Neo4j over the py2neo driver. Moreover, the IA made extensive use of the standard GDSL library of Neo4j in order to compute the abovementioned attributes. The results based on the ROC curve were very encouraging.
... IA design relies heavily on autonomous systems principles [2] or, more recently, on developmental psychology guidelines [3] with additional considerations such as user acceptance [4] having been recently factored in. In order to carry out their tasks efficiently, IAs frequently rely on ML such as case base reasoning [5] or other scehemes to deal with uncertain or evolving digital environments [6] involving multiple users or agents [7]. ...
Presentation
Full-text available
Citation graph mining is an indispensable tool in scientometrics since such graphs succinctly represent researcher collaborations and their respective evolution over time. This allows the discovery of scientific communities and workgroups as well as the the recommendation of potential coworkers. The latter in turn is critical in the flow and formulation of new ideas, either in human led or algorithmically driven efforts. Large citation graphs can be efficiently mined for community structure potential in the form of latent links with high probability of existence by intelligent agents employing a wide array of machine learning techniques, including random forests and neural networks. As a concrete example, the proposed intelligent agent architecture has been tested using a popular bibliometric dataset as a benchmark and a plethora of structural attributes such as the Adamic-Adar score and preferential attachment. The citation graph, containing highly imbalanced numbers of positive and negative examples, was stored in an standalone Neo4j instance, whereas the intelligent agents, implemented in Python, communicated with Neo4j over the py2neo driver. Moreover, the IA made extensive use of the standard GDSL library of Neo4j in order to compute the abovementioned attributes. The results based on the ROC curve were very encouraging.
... Today, artificial intelligence (AI) is pervasive as it influences a lot of different areas in our private and working lives, for instance through AI-based conversational agents, which are applicated in various usage scenarios. AI mimics our human natural intelligence, in this context it can lead an intelligent conversation with a human counterpart (Elshan et al., 2022). Due to the COVID-19 pandemic, the use of intelligent assistants in business has significantly increased and Opus Research refers to 2021 as "The Year of the Ubiquitous Intelligent Assistants" (Miller, 2021). ...
... CAs are able to interact and communicate with other agents, like humans, to achieve goals by knowing their environment as well as memorizing the gained information and to improve their interaction skills through learning. Therefore, CAs can be seen as unique type of Information system (IS) entity, which are characterized by their intelligence and high level of interaction (Elshan et al., 2022). CAs are an increasing research field as traditional software-based systems can be enriched through CAs as an easy-touse link between humans and computers. ...
... Currently, CAs are employed in many different domains (Elshan et al., 2022) to provide direct interaction with users (Kim et al., 2019), foster engagement (Lundqvist et al., 2013), help users to reach their goals (Pérez et al., 2016) through natural conversational interaction (Cassell, 2000) and constant availability with immediate response times (Keyser et al., 2019). One central element of developing effective CAs is human-like design. ...
Conference Paper
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Human-agent interaction is increasingly influencing our personal and work lives through the proliferation of conversational agents in various domains. As such, these agents combine intuitive natural language interactions by also delivering personalization through artificial intelligence capabilities. However, research on CAs as well as practical failures indicate that CA interaction oftentimes fails miserably. To reduce these failures, this paper introduces the concept of building common ground for more successful human-agent interactions. Based on a systematic review our analysis reveals five mechanisms for achieving common ground: (1) Embodiment, (2) Social Features, (3) Joint Action, (4) Knowledge Base, and (5) Mental Model of Conversational Agents. On this basis, we offer insights into grounding mechanisms and highlight the potentials when considering common ground in different human-agent interaction processes. Consequently, we secure further understanding and deeper insights of possible mechanisms of common ground in human-agent interaction in the future.
... Current research is mainly concerned with behavioral aspects of users, CA characteristics, and generating prescriptive knowledge to help design and build aspects like empathy, personality, compassion, and emotions into CAs (Diederich et al. 2022;Elshan et al. 2022;Lisetti et al. 2013;Simon et al. 2021). Moreover, research already describes different forms of CAs, which perform different tasks, occupy different time horizons of interaction, and consequently establish different degrees of interpersonal relationships (Elshan et al. 2022;Nißen et al. 2021). ...
... Current research is mainly concerned with behavioral aspects of users, CA characteristics, and generating prescriptive knowledge to help design and build aspects like empathy, personality, compassion, and emotions into CAs (Diederich et al. 2022;Elshan et al. 2022;Lisetti et al. 2013;Simon et al. 2021). Moreover, research already describes different forms of CAs, which perform different tasks, occupy different time horizons of interaction, and consequently establish different degrees of interpersonal relationships (Elshan et al. 2022;Nißen et al. 2021). Virtual companions are one of these particular forms that, unlike task-oriented CAs, have received little attention in research and, most importantly, less research exists that examines the users of such existing services and their motivations for using them. ...
... They are commonly used for repeatable and mundane tasks such as creating calendar entries, sending messages, or giving reminders. However, some virtual assistants, such as Amazon's Alexa, also have anthropomorphic capabilities like giving advice, providing emotional support, or supporting personal health (Diederich et al. 2022;Elshan et al. 2022;McTear 2018). ...
Conference Paper
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Many people globally experience the feeling of loneliness and struggle with its consequences. A modern way to deal with this loneliness and lack of companionship is to use empathetic and emotional conversational agents. Often referred to as virtual companions, these agents can engage in human-like conversations with their users and build relationships with them through modern artificial intelligence technologies. One established service of such virtual companions is Replika, which we investigate in this study to explore what users expect to gain from long-term interactions with virtual companions and what they tend to talk about with them. Using a text mining approach and 119,831 reviews of the Replika service, we analyze users' sentiments, emotions, and topics. Our results show that users interact with virtual companions to cope with their loneliness and, especially, to address their mental well-being. Furthermore, Replika users have a joyful and beneficial experience during long-term interaction with such virtual companions.
... The Chatbot itself is a piece of software that utilizes Natural Language Processing (NLP), which is a subset of Artificial Intelligence (AI). The model for the Chatbot was derived from Human Computer Interaction (HCI), which allows computers to communicate with humans through text [4]. Because questions will be answered by the chatbot around the clock, prospective students who are looking for information will have a much simpler time finding what they need thanks to the creation of this chatbot [3]. ...
Article
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CS is one of the most important functions of any client-related organization, whether a business or a school (customer service). Notably from the committee responsible for student selection, CS, on the other hand, has a very limited capacity to be handled by humans, which can reduce university satisfaction. Therefore, we require technological assistance, which in this case takes the form of an AI-based chatbot. The objective of this study is to design and develop a chatbot system utilizing NLP (natural language processing) to aid the CS of the new student admissions committee at Pahlawan Tuanku Tambusai University in answering questions from prospective new students. The employed method is dice similarity weighted by TFIDF. The results of the conducted tests indicated that the recall rate was 100 percent and the precision reached 76.92 percent. The evaluation results indicate that the chatbot can effectively respond to questions from prospective students.
... In the following, we will summarize key findings of articles. Elshan et al. (2022) conduct a study entitled "Understanding the Design Elements Affecting User Acceptance of Intelligent Agents: Past, Present and Future " and present a systematic literature review of intelligent agents by studying the design elements affecting user acceptance. Intelligent agents are described as agents that perceive and respond in a timely manner and are capable of interacting with other agents (i.e., humans) and react to their environment. ...
... What all these systems have in common, is that they allow their users to interact with them using natural language, which is why the systems are summarized by the term conversational agent (CA) (Diederich et al., 2022;McTear et al., 2016). There are already various use cases for CAs today, ranging from executing smartphone functions, such as creating calendar entries or sending messages to smart home control, to interaction in the healthcare context (Ahmad et al., 2022;Elshan et al., 2022;Gnewuch et al., 2017;McTear et al., 2016;Sin & Munteanu, 2020). Thus, CAs currently offer a new way of interacting with information technology . ...
... Thus, CAs currently offer a new way of interacting with information technology . Recent literature reviews show a growing interest in CAs and AI-enabled systems (Diederich et al., 2022;Elshan et al., 2022;Nißen et al., 2021;Rzepka & Berger, 2018), but mainly a limited variety of application contexts, which mostly focus on short-term interactions in marketing, sales, and support. Application scenarios that require long-term interaction are available but under-researched (Diederich et al., 2022;Elshan et al., 2022). ...
... Recent literature reviews show a growing interest in CAs and AI-enabled systems (Diederich et al., 2022;Elshan et al., 2022;Nißen et al., 2021;Rzepka & Berger, 2018), but mainly a limited variety of application contexts, which mostly focus on short-term interactions in marketing, sales, and support. Application scenarios that require long-term interaction are available but under-researched (Diederich et al., 2022;Elshan et al., 2022). Additionally, the current applications show that CA's main goal is to provide personal assistant functionality, while less attention goes to the actual interaction with the system which should be improved by social behaviors being incorporated (Elshan et al., 2022;Gnewuch et al., 2017;Krämer et al., 2011;Nißen et al., 2021;Rzepka & Berger, 2018). ...
Article
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Due to significant technological progress in the field of artificial intelligence, conversational agents have the potential to become smarter, deepen the interaction with their users, and overcome a function of merely assisting. Since humans often treat computers as social actors, theories on interpersonal relationships can be applied to human-machine interaction. Taking these theories into account in designing conversational agents provides the basis for a collaborative and benevolent long-term relationship, which can result in virtual companionship. However, we lack prescriptive design knowledge for virtual companionship. We addressed this with a systematic and iterative design science research approach, deriving meta-requirements and five theoretically grounded design principles. We evaluated our prescriptive design knowledge by taking a two-way approach, first instantiating and evaluating the virtual classmate Sarah, and second analyzing Replika, an existing virtual companion. Our results show that with virtual companionship, conversational agents can incorporate the construct of companionship known from human-human relationships by addressing the need to belong, to build interpersonal trust, social exchange, and a reciprocal and benevolent interaction. The findings are summarized in a nascent design theory for virtual companionship, providing guidance on how our design prescriptions can be instantiated and adapted to different domains and applications of conversational agents.
... CAs are software systems that are capable of interacting with humans via natural language in a dialogical fashion (Araujo, 2018;Bittner et al. 2019b;. The concept of CAs is inspired by the idea to emulate naturalistic text-or speech-based conversations between intelligent machines and humans by analogy to human interaction (Elshan et al., 2022;Laumer et al., 2019;McTear et al., 2016). Different terms have been utilized for CAs (e.g., virtual or cognitive agent, dialogue system, and chatbot or chatterbot) referring to the modes of either spoken or written interaction and interactive or static forms of representation (Gnewuch et al., 2017;Hill et al., 2015;Shawar & Atwell, 2007). ...
Article
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Large numbers of incomplete, unclear, and unspecific submissions on idea platforms hinder organizations to exploit the full potential of open innovation initiatives as idea selection is cumbersome. In a design science research project, we develop a design for a conversational agent (CA) based on artificial intelligence to facilitate contributors in generating elaborate ideas on idea platforms where human facilitation is not scalable. We derive prescriptive design knowledge in the form of design principles, instantiate, and evaluate the CA in two successive evaluation episodes. The design principles contribute to the current research stream on automated facilitation and can guide providers of idea platforms to enhance idea generation and subsequent idea selection processes. Results indicate that CA-based facilitation is engaging for contributors and yields well-structured and elaborated ideas.
... However, the scientific literature is dispersed into different thematic axes and research areas [6,7]. Furthermore, the scientific and practical knowledge about CAs has also grown in a dispersed manner, given a shortage of integrative perspectives to support CA development and design processes [8,9]. ...
... To identify a wide range of literature on CAs, the search string is chosen to be rather broad. Based on recent literature reviews (e.g., [6,7]), we identified different keywords researchers used to describe CAs. This resulted in the following search string: "conversational agent" OR "chatbot" OR "chat bot" OR "interactive agent" OR "talkbot" ...
Chapter
Conversational Agents (CAs) provide the means to foster user experience design through seizing their interaction capability, knowledgeability, and human-like behavior. To support practice and academia in designing CAs, IS researchers have been creating design knowledge in the form of design principles (DPs) guided by the Design Science paradigm. However, scientific literature in this vein is dispersed and lacks an axis of cohesion and transferability to sustained practice usage. This raises the question of reusability of design principles in the realm of CAs. Therefore, in this study, we conduct a Systematic Literature Review to retrieve and assess design principles of existing design science papers dealing with CAs with regard to their reusability. Our findings indicate that the Design Science community, in our case in the domain of CAs, seems to face challenges in creating reusable design principles. We discuss this observation and provide avenues on how to move forward.