Exploring the Future Development of Artificial Intelligence (AI) Applications in Chatbots: A Bibliometric Analysis

Liu, Li; Duffy, Vincent G.

doi:10.1007/s12369-022-00956-0

Cited by 13 publications

(6 citation statements)

References 84 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Studies by Lessio and Morris [49] demonstrated that well-designed personas can create deeper emotional resonance with users and foster trust. Zhang et al [99] confirmed the effectiveness of sophisticated persona-driven dialogues, while Chaves and Gerosa [19] showed that persona-infused agents exhibit enhanced social intelligence, thus solidifying user trust and augmenting service value [52]. Yu et al [98] found that user-customized conversational systems achieve better user engagement.…”

Section: Understanding the Effect Of Agent Personasmentioning

confidence: 98%

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Ha,

Jeon,

Han

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

Large language models (LLMs) have facilitated significant strides in generating conversational agents, enabling seamless, contextually relevant dialogues across diverse topics. However, the existing LLM-driven conversational agents have fixed personalities and functionalities, limiting their adaptability to individual user needs. Creating personalized agent personas with distinct expertise or traits can address this issue. Nonetheless, we lack knowledge of how people customize and interact with agent personas. In this

show abstract

Section: Understanding the Effect Of Agent Personasmentioning

confidence: 98%

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

Ha,

Jeon,

Han

et al. 2024

Proceedings of the CHI Conference on Human Factors in Computing Systems

View full text Add to dashboard Cite

show abstract

“…“Machine conversation systems that interact with human users via natural conversational language” is the definition of chatbots, an artificial intelligence-based communication system (Shawar and Atwell, 2005, p. 489). Put differently, chatbots are artificially intelligent machine speech systems that can mimic human speech (Liu and Duffy, 2023) and converse with users in natural language (Brennan, 2006). In the 1960s, chatbots disembodied conversational tools that use text-based interfaces to communicate with humans were initially created with the intention of entertaining users by conversing with them in human language (Chi et al.…”

Section: Literature Reviewmentioning

confidence: 99%

An overview of chatbots in tourism and hospitality using bibliometric and thematic content analysis

Yılmaz,

Şahin-Yılmaz

2024

WHATT

View full text Add to dashboard Cite

PurposeArtificial intelligence is one of the most significant and active fields of study in the last few years. Artificial intelligence-derived robotic technologies known as chatbots are gaining interest from both academic and industry sectors. By analyzing the development and patterns of research on the chatbot phenomena within the tourism field, this study seeks to develop a theoretical framework for the interaction between chatbots and tourism.Design/methodology/approachThe Web of Science (WoS) database’s 33 articles on chatbots related to travel and hospitality were examined between 2019 and 2024 using VOSviewer software for bibliometric and thematic content analysis.FindingsResearch on chatbots for tourism and hospitality appears to be in its early stages. The factors influencing tourists' intentions to use chatbots have been thoroughly researched; the attitudes, perceptions and behavioral intentions of destinations, travel agencies and restaurant patrons regarding chatbots were examined, and it was found that the quantitative research approach was dominant. In addition, the majority of the studies are based on a particular theory or model.Originality/valueThis is one of the first attempts to directly comprehend and depict the interconnected structures of studies on the interaction between chatbots and tourism through the use of network analysis. Furthermore, the study’s findings can offer academics a comprehensive viewpoint and a reference manual for more accurate assessment and oversight of the chatbot-tourism interaction. Regarding the lack of research on the topic and the fragmented structure of the studies that exist, it is imperative to provide both a comprehensive overview and a roadmap for future investigations into the usage of chatbots in the travel and hospitality sector.

show abstract

“…The co-citation frequency of a reference indicates its influence and importance in the research field [81,82]. This indicator can be used to understand which papers in a research field have received extensive attention and citations and which works have had a significant impact on the development of the field [32]. To better comprehend the knowledge structure and research foundation of SRID, we performed a reference co-citation analysis.…”

Section: Knowledge Base Of Sr-hri Researchmentioning

confidence: 99%

“…In the past decade, research in social robot interaction design (SRID) has experienced rapid growth, leading to a substantial body of literature on the subject [16,19,32]. Such a significant growth in the literature requires new approaches to review and analyze trends within knowledge domains [33,34].…”

Section: Introductionmentioning

confidence: 99%

“…Bibliometrics is an effective quantitative method for visualizing and analyzing the existing published literature. It enables the extraction of regular patterns and underlying insights from a large volume of papers, and is now widely employed in numerous scientific research fields [32,37]. Therefore, this paper will use bibliometrics to address the following five research questions to provide a comprehensive overview of SRID: RQ1: What are the main focuses of SRID's current research hotspots?…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Research Hotspots and Trends of Social Robot Interaction Design: A Bibliometric Analysis

Wang,

Chen,

Huo

et al. 2023

Sensors

View full text Add to dashboard Cite

(1) Background: Social robot interaction design is crucial for determining user acceptance and experience. However, few studies have systematically discussed the current focus and future research directions of social robot interaction design from a bibliometric perspective. Therefore, we conducted this study in order to identify the latest research progress and evolution trajectory of research hotspots in social robot interaction design over the last decade. (2) Methods: We conducted a comprehensive review based on 2416 papers related to social robot interaction design obtained from the Web of Science (WOS) database. Our review utilized bibliometric techniques and integrated VOSviewer and CiteSpace to construct a knowledge map. (3) Conclusions: The current research hotspots of social robot interaction design mainly focus on #1 the study of human–robot relationships in social robots, #2 research on the emotional design of social robots, #3 research on social robots for children’s psychotherapy, #4 research on companion robots for elderly rehabilitation, and #5 research on educational social robots. The reference co-citation analysis identifies the classic literature that forms the basis of the current research, which provides theoretical guidance and methods for the current research. Finally, we discuss several future research directions and challenges in this field.

show abstract

Exploring the Future Development of Artificial Intelligence (AI) Applications in Chatbots: A Bibliometric Analysis

Cited by 13 publications

References 84 publications

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

CloChat: Understanding How People Customize, Interact, and Experience Personas in Large Language Models

An overview of chatbots in tourism and hospitality using bibliometric and thematic content analysis

Research Hotspots and Trends of Social Robot Interaction Design: A Bibliometric Analysis

Contact Info

Product

Resources

About