An Integrated Theory for Chatbot Use in Air Travel: Questionnaire Development and Validation

Trapero, Hazel; Ilao, Joel P.; Lacaza, Rutcher

doi:10.1109/tencon50793.2020.9293710

Cited by 10 publications

(6 citation statements)

References 20 publications

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“…Since deep learning architectures have a high variance and a low bias, a simple averaging of the ensemble models improves the generalization performance by reducing the variance among the models. The averaging of the base learners is conducted directly on the outputs of the base learners, or on the predicted probabilities of the classes using the Softmax function [ 88 ] illustrated in Equation (1).

where

is the probability of the i -th unit on the j -th base learner,

is the output of the ith unit of the j -th base learner, and K is the number of classes.…”

Section: Methodsmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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A person infected with drug-resistant tuberculosis (DR-TB) is the one who does not respond to typical TB treatment. DR-TB necessitates a longer treatment period and a more difficult treatment protocol. In addition, it can spread and infect individuals in the same manner as regular TB, despite the fact that early detection of DR-TB could reduce the cost and length of TB treatment. This study provided a fast and effective classification scheme for the four subtypes of TB: Drug-sensitive tuberculosis (DS-TB), drug-resistant tuberculosis (DR-TB), multidrug-resistant tuberculosis (MDR-TB), and extensively drug-resistant tuberculosis (XDR-TB). The drug response classification system (DRCS) has been developed as a classification tool for DR-TB subtypes. As a classification method, ensemble deep learning (EDL) with two types of image preprocessing methods, four convolutional neural network (CNN) architectures, and three decision fusion methods have been created. Later, the model developed by EDL will be included in the dialog-based object query system (DBOQS), in order to enable the use of DRCS as the classification tool for DR-TB in assisting medical professionals with diagnosing DR-TB. EDL yields an improvement of 1.17–43.43% over the existing methods for classifying DR-TB, while compared with classic deep learning, it generates 31.25% more accuracy. DRCS was able to increase accuracy to 95.8% and user trust to 95.1%, and after thae trial period, 99.70% of users were interested in continuing the utilization of the system as a supportive diagnostic tool.

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where

is the probability of the i -th unit on the j -th base learner,

is the output of the ith unit of the j -th base learner, and K is the number of classes.…”

Section: Methodsmentioning

confidence: 99%

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

et al. 2022

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“…Regarding the effect of performance expectancy on the behavioral intention to use chatbots (H1), it was demonstrated that it had a positive impact on the behavioral intention to adopt and use chatbots [17,52,54,59,60,119]. AI agents enable users to increase productivity, enjoy better and more effective service delivery, and receive responses to requests saving time and effort.…”

Section: Discussionmentioning

confidence: 99%

“…Familiar groups, such as family and relatives, friends, colleagues, and fellow students, who influence others to adopt a technology, play a major role in the intention to adopt a new technology [58]. Overall, various studies have confirmed the impact of the specific variable on the behavioral intention to use chatbots [59,60]. Thus, the following hypothesis is formulated: H3: social influence positively (SI) affects behavioral intention to adopt chatbots.…”

Section: Utaut 2 Variablesmentioning

confidence: 99%

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Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants

Paraskevi,

Saprikis,

Avlogiaris

2023

Human Behavior and Emerging Technologies

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Artificial intelligence agents (chatbots), which are programs to communicate with users primarily in customer service contexts, are an alternative interaction channel supporting businesses in the digital environment and vital components in customer service. The present empirical paper, which is aimed at identifying and discussing the factors motivating nonusers to adopt the specific technology in mobile contexts, proposes a comprehensive conceptual model, which combines the UTAUT 2 behavioral theory with variables of mobile service quality contexts, such as information quality, privacy concerns, interface, and equipment, as well as trust and mobility factors. Data analysis, based on the partial least squares structural equation modeling (PLS-SEM) statistical method, revealed that performance expectancy, facilitating factors, hedonic motivation, mobility, trust, and service quality positively affect nonusers’ behavioral intention to adopt chatbots. In addition, equipment, interface, and trust have a significant impact on users’ trust in the context of mobile chatbots. Personal data privacy issues also have a negative effect on trust, in contrast to effort expectancy, which positively affects performance expectancy. As mobile service quality factors have not been investigated before in the context of chatbots, the findings of the present research are expected to provide useful insights both to academia and the business industry.

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“…Remarkably, a significant number of researchers have focused on the intention to adopt and use chatbots by investigating factors which affect users in specific research areas, such as health [13], financial services [14][15][16], tourism [5,[17][18][19][20], customer service (e.g. Refs [1,[21][22][23][24][25][26][27][28][29][30][31][32][33][34]), mobile commerce [35][36][37], business [38,39], insurance [12,40] and education [41,42].…”

Section: Users' Intention Toward Chatbots' Adoptionmentioning

confidence: 99%

A literature review on users' behavioral intention toward chatbots' adoption

Gatzioufa

Saprikis

2022

ACI

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PurposeDespite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots.Design/methodology/approachThe research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots.FindingsAfter a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution.Originality/valueAs far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

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An Integrated Theory for Chatbot Use in Air Travel: Questionnaire Development and Validation

Cited by 10 publications

References 20 publications

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Embedded AMIS-Deep Learning with Dialog-Based Object Query System for Multi-Class Tuberculosis Drug Response Classification

Modeling Nonusers’ Behavioral Intention towards Mobile Chatbot Adoption: An Extension of the UTAUT2 Model with Mobile Service Quality Determinants

A literature review on users' behavioral intention toward chatbots' adoption

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