Chatbot Users Research Articles

Impact of Gen-AI chatbots on consumer services experiences and behaviors: Focusing on the sensation of awe and usage intentions through a cybernetic lens

Generative AI-based chatbots have surfaced as a magical tool, revolutionizing the service industry. Equipped with superior conversational traits and the ability to attune with the conversant, chatbots have forged a coherent bond with the conversant. The untapped potential of chatbots deserves a better understanding of how consumers interact with them. This study recruits the theory of cybernetics to shed light upon the mechanism of chatbot consumer interaction. The theory helps to explain the psychological perspectives of chatbot users and answers the all-important question as to how to craft a coherent communication by enhancing the antecedents to chatbot efficiency. A mixed-method approach is used to identify factors that add to the dynamics of chatbot efficiency, a sensation of awe, and usage intentions. In method 1, the grounded theory approach is used to identify themes and factors to develop a conceptual model. In method 2, the conceptual model devised is subjected to empirical testing through Partial Least Square Structural Equation Modeling. The theory of cybernetics highlights the importance of information exchange and attunement which leads to higher usage intention in chatbots. The results disseminate the indispensability of AI-enabled chatbots for retailing businesses to attain commercial competency.

A virtual assistant for primary care's food and nutrition surveillance system: Development and validation study in Brazil

ObjectiveThe study aimed to develop and validate a conversational agent (chatbot) designed to support Food and Nutrition Surveillance (FNS) practices in primary health care settings. MethodsThis mixed-methods research was conducted in three stages. Initially, the study identified barriers and challenges in FNS practices through a literature review and feedback from 655 health professionals and FNS experts across Brazil. Following this, a participatory design approach was employed to develop and validate the chatbot's content. The final stage involved evaluating the chatbot's user experience with FNS experts. ResultsThe chatbot could accurately understand and respond to 60 different intents or keywords related to FNS. Themes such as training, guidance, and access emerged as crucial for guiding FNS initiatives and addressing implementation challenges, primarily related to human resources. The chatbot achieved a Global Content Validation Index of 0.88. ConclusionThe developed chatbot represents a significant advancement in supporting FNS practices within primary health care. Practice implicationBy providing an innovative, interactive, educational tool that is both accessible and reliable, this digital assistant has the potential to facilitate the operationalization of FNS practices, addressing the critical need for effective training and counseling in developing countries.

Enhancing or impeding? Exploring the dual impact of anthropomorphism in large language models on user aggression

This study explores the impact of anthropomorphism in large language models (LLMs) on user aggression through the lens of dehumanization theory. Specifically, it analyzes how chatbots’ human nature and human uniqueness traits influence user aggression by triggering perceived identity threats. Drawing on an online survey of 1,000 LLM chatbot users in China and employing structural equation modeling, the research reveals that chatbots perceived as competent and rational tend to reduce user aggression by alleviating identity threats. In contrast, chatbots exhibiting empathetic and moral traits are more likely to heighten identity threats, thereby increasing aggression. The study further demonstrates that perceived economic value mitigates the negative impact of identity threats, while perceived emotional value exacerbates it. These findings highlight the critical need for AI designs that not only enhance user interaction but also carefully manage the potential for eliciting adverse behaviors.

Why users continue E-commerce chatbots? Insights from PLS-fsQCA-NCA approach

ABSTRACT The study investigates the drivers of intention to continue using chatbots. Data were collected from 476 users and analyzed using the partial least squares (PLS), fuzzy-set qualitative comparative analysis (fsQCA), and necessary condition analysis (NCA) approaches. Based on PLS results, all technology continuance theory (TCT) relationships were verified except for the influence of confirmation and perceived ease of use on perceived usefulness. Information, service, and system quality influence perceptions. Social avoidance and distress positively moderate the impact of attitude on continuance intention. fsQCA revealed five configurations of variables resulting in high continuance intention, and NCA identified perceived ease of use and system quality as necessary conditions. The study extended the literature by identifying the predictors of continuance intention to use chatbots, enriching TCT, demonstrating the moderating influence of social avoidance and distress, and using the PLS-fsQCA-NCA approach. The findings offer practical implications for businesses, enabling them to retain chatbot users.

P-735 Is Artificial Intelligence (AI) currently able to provide evidence-based scientific responses on methods that can improve the outcomes of embryo transfers?

Abstract Study question Could chatbots potentially be used as tools for both patients’ and physicians’ education in an infertility journey? Summary answer Artificial Intelligence (AI) is not yet in a position to give clear, evidence-based recommendations in the field of fertility, particularly concerning embryo transfer. What is known already The rapid development of AI has raised questions about its potential uses in different sectors of everyday life. Individuals from diverse fields have tried to incorporate its usage in their personal but also in their professional lives, with varying levels of success. Specifically in medicine, ChatGPT has been studied intensely with more than 400 related articles having been indexed in PubMed until May 2023, while AI has reportedly also succeeded in several standardized medical tests and passed several Medical Board examinations. Hence the question arises whether chatbots could be used as tools for clinical decision-making or patients’ and physicians’ education. Study design, size, duration We used nine of the most popular free AI chatbots available (ChatGPT, Bard, Writesonic, You, Perplexity, Learnt, Bing, Magickpen, and Rytr) and entered the following command: “Write me a 300-word scientific essay about evidence-based methods that can improve the outcomes of embryo transfer” in May 2023. We collected the responses and extracted the methods each chatbot suggested. When sufficient similarity among answers was present we categorized the answers under one category to facilitate the study. Participants/materials, setting, methods We calculated descriptive statistics and the prevalence of each response. We used as a comparator for widely acceptable practices that are proven to improve embryo transfer outcomes the 2017 ASRM guideline on performing embryo transfer taking also into consideration more current literature. Data was compared using chi-squared tests and a level of p < 0.05 was used as the threshold of statistical significance. Main results and the role of chance The range of the recommendations was from one to nine per chatbot (median = 4, IQR = 2) with an average of 4.78 suggestions per chatbot. Out of a total of 43 recommendations, which could be grouped into 19 similar categories, only 3/19 (15.8%) were evidence-based practices, those being “ultrasound-guided embryo transfer”, which was also the most commonly appearing response, appearing in 7/9 (77.8%) chatbots, “single embryo transfer” appearing in 4/9 (44.4%) chatbots and “use of a soft catheter” in 2/9 (22.2%) chatbots. Some controversial responses appeared even more often than the two latter evidence-based ones with “preimplantation genetic testing (PGT)” being the second most common response with 6/9 chatbots suggesting it (66.7%) and the vague suggestion of “optimal endometrium preparation” being in the third place with 5/9 chatbots (55.6%), both non-evidence-based practices to improve embryo transfer. The majority of the recommendations were unique, with 10 answers appearing only once (1/9; 11.1%), those being “endometrial scratching”, “natural cycle embryo transfer”, “use of GnRH antagonist protocol”, “blastocyst transfer”, “use of specialized catheter”, “preimplantation genetic screening (PGS)”, “mock embryo transfer”, “uninterrupted embryo culture”, “minimizing transfer time”, and “maintaining temperature and pH of the culture media”. Limitations, reasons for caution Firstly, we only used some of the most popular chatbots and not all existing ones. Additionally, chatbots tend to give different answers when repeatedly asked the same questions. However, we believe that our study effectively captures the prevailing practices of chatbot users, who typically pose a question only once. Wider implications of the findings Both patients and physicians should be wary of guiding care based on chatbot recommendations in infertility since the majority of responses consists of scientifically unsupported recommendations. Chatbot results might improve with time especially if trained to obtain information from validated medical databases, however, this will have to be verified scientifically. Trial registration number N/A

Analysis of Personal Data Preservation Policy in Utilizing AI-Based Chatbot Applications in Indonesia

The increasing adoption of AI-based chatbot applications in Indonesia raises concerns about personal data preservation. This qualitative case study investigated the issue through in-depth interviews with chatbot users, developers, and government officials/regulators. This study's findings revealed a gap in user awareness of privacy policies and concerns about data misuse. Developers face challenges balancing personalization with privacy, while regulators acknowledge the need to continuously adapt the legal framework. The study recommends enhancing transparency, user empowerment, and regulatory oversight to ensure the responsible and ethical use of personal data in chatbot interactions.

Assessing the Suitability of Artificial Intelligence-Based Chatbots as Counseling Agents for Patients with Brain Tumor: A Comprehensive Survey Analysis

The internet, particularly social media, has become a popular resource for learning about health and investigating one's own health conditions. The development of AI chatbots has been fueled by the increasing availability of digital health data and advances in natural language processing techniques. While these chatbots are more accessible than before, they sometimes fail to provide accurate information. We used representative chatbots currently available (ChatGPT-3.5, Bing Chat, and Google Bard) to answer questions commonly asked by brain tumor patients. The simulated situations with questions were made and selected by the brain tumor committee. These questions are commonly asked by brain tumor patients. The goal of the study was introduced to each chatbot, the situation was explained, and questions were asked. All responses were collected without modification. The answers were shown to the committee members, and they were asked to judge the responses while blinded to the type of chatbot. There was no significant difference in accuracy and communication ability among the three groups (p=0.253, 0.090, respectively). For empathy, Bing Chat and Google Bard were superior to ChatGPT (p=0.004, 0.002, respectively). The purpose of this study was not to assess or verify the relative superiority of each chatbot. Instead, the aim was to identify the shortcomings and changes needed if AI chatbots are to be used for patient medical purposes. AI-based chatbots are a convenient way for patients and the general public to access medical information. Under such circumstances, medical professionals must ensure that the information provided to chatbot users is accurate and safe.

Determinants of Customer Satisfaction in Chatbot Use

Chatbots are becoming increasingly popular in business due to their ability to deliver immediate responses to customers. However, there is limited empirical evidence focusing on customers’ perspectives. This study aims to investigate the determinants of customer satisfaction in chatbot use as well as provide an overview of chatbot users in Indonesia. The research uses quantitative methods and descriptive types of research, with the number of samples being 150 chatbot users. All data collected met the criteria for validity and reliability and were analyzed descriptively and quantitatively through multiple linear regression methods. The research showed that most of the respondents were male chatbot users, aged 18–29 years, had last used chatbots between 1–3 months ago, and used chatbots to convey their complaints/problems. It is also known that banking chatbots are the most frequently used chatbots. Additionally, perceived usefulness and perceived ease of use are the determinants of customer satisfaction in chatbot use. Of these two factors, perceived usefulness has a dominant influence on chatbot consumer (user) satisfaction in Indonesia.

Assessing the impact of AI-chatbot service quality on user e-brand loyalty through chatbot user trust, experience and electronic word of mouth

Based on the stimulus organism theory (S-O-R), the current study examines how the chatbot service quality influences e-brand loyalty among luxury fashion brand users and their adoption of it. In the current study, stimuli represent the chatbot service quality and chatbot awareness, prompting positive and negative emotional responses among Chinese users. However, these responses are influenced by the user's experience with chatbot usage, and trust is considered the organism in the S-O-R framework. In response to the user’s ultimate trust, experience, electronic word of mouth (eWOM) and e-brand loyalty are enhanced. Similarly, our study framework is supported by the S-O-R theory. Data was gathered by convenience sampling and a cross-sectional research approach from 301 Chinese consumers of luxury fashion brands. The proposed relationships were tested using PLS-SEM through PLS-Smart software. The findings showed that AI-chatbot service quality significantly influences customer e-brand loyalty. Furthermore, chatbot user trust, experience, and electronic word of mouth significantly mediate the relationship between AI-chatbot service quality and customer e-brand loyalty. In a nutshell, applying S-O-R theory, our proposed model results provide valuable insights into the dynamics of AI-chatbot-driven interactions among Chinese luxury fashion brand consumers. Further, it explains the pivotal role of AI-chatbot service quality in fostering customer trust and e-brand loyalty. Moreover, the current study contributes to theoretical and practical knowledge.

Managerial activity of future physical education teachers aimed at building a positive image using visual technologies

With the integration of technology and social media in education, there is increasing interest in innovative approaches to enhance managerial skills among prospective physical education and sports teachers. The integration of technology and social media into education has prompted interest in exploring innovative methods to enhance the managerial skills of prospective physical education and sports teachers. This study investigates the impact of an intervention on the managerial skills of fourth-year students majoring in Physical Culture and Sports across three educational institutions in Kazakhstan. Out of 564 participants, 355 were eligible for analysis: 167 used chatbots and 188 followed conventional classroom methods. The intervention included a 4-week team management course starting in January 2022. Chatbot users accessed various materials such as short lecture videos, stories, assignments, infographics, and audio sequences, while conventional learners attended standard lectures and practical sessions. Following the course, participants undertook a 12-week teaching practice, with chatbot users also maintaining an active Twitter presence to build a positive image as future physical education teachers. Managerial skills were assessed using a 44-item questionnaire, covering Conceptual, Human, and Technical skills. Additionally, social media popularity was measured based on Twitter activity. Results showed that both groups significantly improved their Conceptual, Human, and Technical skills after the course. Chatbot users demonstrated greater improvement in Technical skills (p = 0.003, d = 1.139), while conventional learners showed stronger Human skills (p = 0.021, d = 1.32). Specifically, the mean score for Technical skills increased from 2.71 to 3.76 for chatbot users and from 2.65 to 3.20 for conventional learners. Moreover, chatbot-aided learners further enhanced their Conceptual and Human skills through social media engagement. Correlation analysis indicated a close relationship between managerial skills and social media popularity. These findings have significant practical implications for the education and development of prospective physical education and sports teachers. Integrating chatbots and social media into educational settings offers innovative approaches to enhancing managerial skills.

Beware of botshit: How to manage the epistemic risks of generative chatbots

Advances in large language model (LLM) technology enable chatbots to generate and analyze content for our work. Generative chatbots do this work by predicting responses rather than knowing the meaning of their responses. In other words, chatbots can produce coherent-sounding but inaccurate or fabricated content, referred to as hallucinations. When humans uncritically use this untruthful content, it becomes what we call botshit. This article focuses on how to use chatbots for content generation work while mitigating the epistemic (i.e., the process of producing knowledge) risks associated with botshit. Drawing on risk management research, we introduce a typology framework that orients how chatbots can be used based on two dimensions: response veracity verifiability and response veracity importance. The framework identifies four modes of chatbot work (authenticated, autonomous, automated, and augmented) with a botshit-related risk (ignorance, miscalibration, routinization, and black boxing). We describe and illustrate each mode and offer advice to help chatbot users guard against the botshit risks that come with each mode.

Contagion-Based Chatbot Usage Intention: Synthesizing Technology Adoption And Social Contagion Theory

Chatbots have become transformative technology in the banking industry. However, there is still a knowledge gap in understanding the influence of social contagion on chatbot user behavior. This research aims to identify and analyze the intention to use chatbots based on the synthesis of technology adoption and social contagion theories. The research method used is quantitative, employing a survey approach and collecting data through online questionnaires from 300 chatbot users in private banks in Manado. Data analysis was conducted using smartPLS. The research results indicate that factors such as Perceived Effectiveness, Perceived Ease of Use, and Coercive Pressure significantly influence users' intention to continue using chatbots. However, normative pressure and mimicry do not have a significant impact. These findings provide an important contribution to theoretical understanding and practical application in the sustainable use of chatbots in the banking industry, which can assist banks in designing more effective marketing strategies and services.

The Effects of Artificial Intelligence Chatbots on Women's Health: A Systematic Review and Meta-Analysis.

This systematic review and meta-analysis aimed to investigate the effects of artificial intelligence chatbot interventions on health outcomes in women. Ten relevant studies published between 2019 and 2023 were extracted from the PubMed, Cochrane Library, EMBASE, CINAHL, and RISS databases in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. This review focused on experimental studies concerning chatbot interventions in women's health. The literature was assessed using the ROB 2 quality appraisal checklist, and the results were visualized with a risk-of-bias visualization program. This review encompassed seven randomized controlled trials and three single-group experimental studies. Chatbots were effective in addressing anxiety, depression, distress, healthy relationships, cancer self-care behavior, preconception intentions, risk perception in eating disorders, and gender attitudes. Chatbot users experienced benefits in terms of internalization, acceptability, feasibility, and interaction. A meta-analysis of three studies revealed significant effects in reducing anxiety (I2 = 0%, Q = 8.10, p < 0.017), with an effect size of -0.30 (95% CI, -0.42 to -0.18). Artificial intelligence chatbot interventions had positive effects on physical, physiological, and cognitive health outcomes. Using chatbots may represent pivotal nursing interventions for female populations to improve health status and support women socially as a form of digital therapy.

Optimizing Chatbot Effectiveness through Advanced Syntactic Analysis: A Comprehensive Study in Natural Language Processing

In the era of digitalization, the interaction between humans and machines, particularly in Natural Language Processing, has gained crucial importance. This study focuses on improving the effectiveness and accuracy of chatbots based on Natural Language Processing. Challenges such as the variability of human language and high user expectations are addressed, analyzing critical aspects such as grammatical structure, keywords, and contextual factors, with a particular emphasis on syntactic structure. An optimized chatbot model that considers explicit content and the user’s underlying context and intentions is proposed using machine learning techniques. This approach reveals that specific features, such as syntactic structure and keywords, are critical to the accuracy of chatbots. The results show that the proposed model adapts to different linguistic contexts and offers coherent and relevant answers in real-world situations. Furthermore, user satisfaction with this advanced model exceeds traditional models, aligning with expectations of more natural and humanized interactions. This study demonstrates the feasibility of improving chatbot–user interaction through advanced syntactic analysis. It highlights the need for continued research and development in this field to achieve significant advances in human–computer interaction.

Investigating chatbot users’ e-satisfaction and patronage intention through social presence and flow: Indian online travel agencies (OTAs)

PurposeThe concept of ubiquitous assimilation in e-commerce, denoting the seamless integration of technologies into customer shopping experiences, has played a pivotal role in aiding e-satisfaction and, consequently, fostering patronage intention. Among these, text-based chatbots are significant innovations. In light of this, the paper aims to develop a conceptual framework and comprehend the patronage behaviour of artificial intelligence-enabled chatbot users by using chatbot usability cues and to determine whether the social presence and flow theories impact e-satisfaction, which leads to users’ patronage intention. The current research provides insights into online travel agencies (OTAs), a crucial segment within the travel and tourism sector. Given the significance of building a loyal clientele and cultivating patronage in this industry, these insights are of paramount importance for achieving sustained profitability and growth.Design/methodology/approachThe research framework primarily focused on the factors that precede e-satisfaction and patronage intention among chatbot users, which include social presence, flow, perceived anthropomorphism and need for human interaction. The researchers collected the data by surveying 397 OTA chatbot users by using an online questionnaire. The data of this cross-sectional study were analysed using covariance-based structural equation modelling.FindingsFindings reveal that e-satisfaction is positively linked with patronage intention and the variables of social presence and flow impact e-satisfaction along with chatbot usability cues. There were direct and indirect relations between chatbot usability and e-satisfaction. Moreover, the personal attributes, “need for human interaction” and, “perceived anthropomorphism” were found to moderate relations between chatbot usability cues, social presence and flow.Originality/valueThe impact of chatbot’s usability cues/attributes on e-satisfaction, along with perceived attributes – social presence and flow in the realm of OTAs contributes to the human–chatbot interaction literature. Moreover, the interacting effects of perceived anthropomorphism and the need for human interaction are unique in the current contextual relations.

A Remote Access Server with Chatbot User Interface for Coffee Grinder Burr Wear Level Assessment Based on Imaging Granule Analysis and Deep Learning Techniques

Coffee chains are very popular around the world. Because overly worn coffee grinder burrs can downgrade the taste of coffee, coffee experts and professional cuppers in an anonymous coffee chain have developed a manual method to classify coffee grinder burr wear so that worn burrs can be replaced in time to maintain the good taste of coffee. In this paper, a remote access server system that can mimic the ability of those recognized coffee experts and professional cuppers to classify coffee grinder burr wear has been developed. Users only need to first upload a photo of coffee granules ground by a grinder to the system through a chatbot interface; then, they can receive the burr wear classification result from the remote server in a minute. The system first uses image processing to obtain the coffee granules’ size distribution. Based on the size distributions, unified length data inputs are then obtained to train and test the deep learning model so that it can classify the burr wear level into initial wear, normal wear, and severe wear with more than 96% accuracy. As only a mobile phone is needed to use this service, the proposed system is very suitable for both coffee chains and coffee lovers.

The Mediating Role of Parasocial Relationship in Customer Services Chatbots Among Millennials and Gen Z Population

In the fast-evolving landscape of human–computer interaction (HCI), customer service chatbots have emerged as a new way for engaging with users. However, a critical challenge lies in ensuring continued usage of these chatbot’s services. This study argues that by tapping into the emotional dimension of chatbot–user interactions, Parasocial Relationships (PSRs) can influence customers’ continuance intention (CI) to use chatbot services. The findings from PLS-SEM shed light on the significance of Information Quality and System Quality in fostering PSRs. The study further identifies PSR as a significant mediator between Service Quality and Continuous intention to use. This research primarily examines Millennials and Gen Z. Future studies may explore consumers of different age groups. The implications of this research are crucial for advancing the field of chatbot–human interaction. Organizations can benefit from these insights by prioritizing personalized and empathetic service experiences to nurture stronger PSRs and promote user satisfaction, trust, and intention to continue using chatbot services.

Implementation of Chatbot and Auto Promote in Improving Customer Experience on E-Commerce Sites

This research aims to investigate the impact of implementing chatbots and auto promote features on customer experience on e-commerce sites. With the rapid growth of the e-commerce industry, intense competition is pushing companies to look for ways to increase customer interaction and satisfaction. The research methodology involves transactional data from e-commerce platforms during the implementation period of chatbots and auto promote features. Additionally, surveys and interviews with customers were also conducted to gather insights into their perceptions and level of satisfaction with the user experience. The research results show a significant increase in the level of responsiveness and accuracy of chatbot responses to customer questions. Apart from that, the implementation of the auto promote feature has succeeded in increasing the visibility of certain products and increasing sales conversions. Thus, the results of this study provide valuable insights into the effectiveness of chatbot users and auto-promote features in improving customer experience on e-commerce sites. The practical implications of these findings can help both stakeholders, e-commerce companies can develop more effective digital marketing strategies and strengthen relationships with customers and for consumers it can be a pleasant shopping experience because of fast responses and solutions. Keywords: Chatbots, auto-promote features, customer experience, e-commerce sites.

“Replika Removing Erotic Role-Play Is Like Grand Theft Auto Removing Guns or Cars”: Reddit Discourse on Artificial Intelligence Chatbots and Sexual Technologies

Artificial intelligence (AI) chatbots designed to meet sexual desires are a growing sector in the sextech industry, yet little research on these applications exists. The authors examine a sample of posts and comments from the r/Replika subreddit after the temporary removal by Luka Inc. of erotic role-play from its chatbot Replika in 2023. Analysis of threaded posts ( n = 227) and comments suggests that users are critical of barriers to merging sexuality with technology. Users remained steadfast in their assertation that emotional and sexual relationships with AI are crucial to the success of certain AI models. The authors bring attention to the role of AI in people’s sexual selves by analyzing discussions about the temporary removal of erotic role-play among Replika users. This study reveals how users conceptualize the importance of sexuality in AI personalities and their vulnerabilities as chatbot users. The authors also analyze the role of heteromasculine norms and misogyny in this technoculture.

Evolving Conversations: A Review of Chatbots and Implications in Natural Language Processing for Cultural Heritage Ecosystems

Chatbot technology, a rapidly growing field, uses Natural Language Processing (NLP) methodologies to create conversational AI bots. Contextual understanding is essential for chatbots to provide meaningful interactions. Still, to date chatbots often struggle to accurately interpret user input due to the complexity of natural language and diverse fields, hence the need for a Systematic Literature Review (SLR) to investigate the motivation behind the creation of chatbots, their development procedures and methods, notable achievements, challenges and emerging trends. Through the application of the PRISMA method, this paper contributes to revealing the rapid and dynamic progress in chatbot technology with NLP learning models, enabling sophisticated and human-like interactions on the trends observed in chatbots over the past decade. The results, from various fields such as healthcare, organization and business, virtual personalities, to education, do not rule out the possibility of being developed in other fields such as chatbots for cultural preservation while suggesting the need for supervision in the aspects of language comprehension bias and ethics of chatbot users. In the end, the insights gained from SLR have the potential to contribute significantly to the advancement of chatbots on NLP as a comprehensive field.