Measuring perception towards AI-based chatbots in Insurance Sector

Mangla, Dolly; Aggarwal, Renu; Maurya, Mohit

doi:10.1109/iitcee57236.2023.10091024

Cited by 4 publications

(3 citation statements)

References 17 publications

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“…However, contrasting results were observed in the area of AI-enabled Internet banking assistants, where Gen Z did not perceive any advantage in using them [63]. In a different context, a recent study [64] has observed that Gen Z users had little knowledge or experience with AI systems, but they typically had a favorable opinion of AIbased virtual assistants for health insurance. Similarly, a cross-cultural study [65] including 48 countries and eight regions observed the significant influence of socio-cultural factors, such as religions and regions on the acceptance of non-conscious data collection of AI assistants, indicating the varying perceptions of factors such as privacy and security associated with AI assistants.…”

Section: Discussionmentioning

confidence: 94%

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Alanzi,

Alsalem,

Alzahrani

et al. 2023

Cureus

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Study purpose: This study aims to analyze various influencing factors among generations X (Gen X), Y (Gen Y), and Z (Gen Z) of artificial intelligence (AI)-powered mental health virtual assistants. Methods: A cross-sectional survey design was adopted in this study. The study sample consisted of outpatients diagnosed with various mental health illnesses, such as anxiety, depression, schizophrenia, and behavioral disorders. A survey questionnaire was designed based on the factors (performance expectancy, effort expectancy, social influence, facilitating conditions, and behavioural intention) identified from the unified theory of acceptance and use of the technology model. Ethical approval was received from the Ethics Committee at Imam Abdulrahman Bin Faisal University, Saudi Arabia. Results: A total of 506 patients participated in the study, with over 80% having moderate to high experience in using mental health AI assistants. The ANOVA results for performance expectancy (PE), effort expectancy (EE), social influence (SI), facilitating conditions (FC), and behavioral intentions (BI) indicate that there are statistically significant differences (p < 0.05) between the Gen X, Gen Y, and Gen Z participants. Conclusion: The findings underscore the significance of considering generational differences in attitudes and perceptions, with Gen Y and Gen Z demonstrating more positive attitudes and stronger intentions to use AI mental health virtual assistants, while Gen X appears to be more cautious.

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Section: Discussionmentioning

confidence: 94%

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Alanzi,

Alsalem,

Alzahrani

et al. 2023

Cureus

View full text Add to dashboard Cite

show abstract

“…They will be proactively participating in the chatbot related policies development and implementation. Most of the past studies have either considered patients (20,23,29) or the clinicians (25) or other healthcare industry workers (30) for perception and attitude analysis. Thus, this study provides an insight about the thought-process of a unique sub-group of population.…”

Section: Resultsmentioning

confidence: 99%

“…The key message from the study is that simulating human behavior and sentiment in a chatbot is still a challenge; the same has been reported by other studies (26,27) . The past literature suggests the lack of humanoid behavior by AI and intangibility of the chatbot explains the lower satisfaction of users towards the sentimental aspect of the chatbot (30) . Amongst the technical parameters, the present study observed that interoperability of the chatbot was the only factor that had a considerably greater proportion of lower scores.…”

Section: Resultsmentioning

confidence: 99%

Identification of Predictors for Utilization of Artificial Intelligence Powered COVID -19 Chatbot for Self-Screening and Health Counselling

Bhola,

Afzal,

Kumar

2023

IJST

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Background: Advent of COVID-19 has led to many challenges to the healthcare system. To deal with the burdening of healthcare and for assuaging the selfscreening process 'Dr. Chhaya'-AI (Artificial Intelligence) based chatbot was developed and implemented by a health research institute. Objectives: First, to capture the perception of the PG (Post-Graduate) students enrolled in public health programmes towards the 'Dr. Chhaya' chatbot. Secondly, to identify and analyze the predictors for the future use of the chatbot. We proposed a hypothesis that students with different levels of technological proficiency have different perceptions towards AI-based health chatbot. Methods: A cross-sectional study on 219 PG students was conducted, using a pre-tested questionnaire. The tool consisted of 2 parts, namely, (i) demographics (ii) experience and perception towards the chatbot. Twelve critical variables were identified which were grouped into 3 domains, namely, 'utility factors', 'sentimental factors', and 'technical appropriateness'. Responses regarding each variable were recorded using an 11-point scale. Statistical analysis of responses was done using IBM-SPSS (ver. 22). Findings: The perception of participants towards AI-based chatbot was found to be positive (overall mean of scores=7.1). Regression analysis revealed that 'utility factor' (β =0.45, p value<0.001) and 'sentimental factor' (β =0.35, p value=0.033) are predictors of future use of the chatbots by participants. Analysis revealed that the proposed hypothesis is found true (at 95% confidence). Novelty: The present paper offers an interdisciplinary approach and provides insights for developing more efficient self-health screening chatbots. The study informs about the factors that augment the AI powered chatbot use as not discussed much in past studies. Findings suggest, policymakers could implement chatbot utilization policy for urban areas, to promote the self-screening process by masses to reduce burden on the healthcare system.

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Effects of Generative Chatbots in Higher Education

Ilieva,

Yankova,

Klisarova-Belcheva

et al. 2023

Information

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Learning technologies often do not meet the university requirements for learner engagement via interactivity and real-time feedback. In addition to the challenge of providing personalized learning experiences for students, these technologies can increase the workload of instructors due to the maintenance and updates required to keep the courses up-to-date. Intelligent chatbots based on generative artificial intelligence (AI) technology can help overcome these disadvantages by transforming pedagogical activities and guiding both students and instructors interactively. In this study, we explore and compare the main characteristics of existing educational chatbots. Then, we propose a new theoretical framework for blended learning with intelligent chatbots integration enabling students to interact online and instructors to create and manage their courses using generative AI tools. The advantages of the proposed framework are as follows: (1) it provides a comprehensive understanding of the transformative potential of AI chatbots in education and facilitates their effective implementation; (2) it offers a holistic methodology to enhance the overall educational experience; and (3) it unifies the applications of intelligent chatbots in teaching–learning activities within universities.

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Measuring perception towards AI-based chatbots in Insurance Sector

Cited by 4 publications

References 17 publications

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

AI-Powered Mental Health Virtual Assistants Acceptance: An Empirical Study on Influencing Factors Among Generations X, Y, and Z

Identification of Predictors for Utilization of Artificial Intelligence Powered COVID -19 Chatbot for Self-Screening and Health Counselling

Effects of Generative Chatbots in Higher Education

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