Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots

Huang, Stanley Y. B.; Lee, Chih-Jen; Lee, Shih-Chin

doi:10.3390/s21175687

Cited by 14 publications

(8 citation statements)

References 67 publications

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“…The sentiments of bots can sometimes result in inaccurate predictions. As such, there arises a need for social bot detection to obtain better predictions [161]. Investigators, analysts, and researchers are continuously reporting the potential dangers brought about by social bots.…”

Section: Challenges and Open Issuesmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

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With the advent of technological marvels like global digitization, the prediction of the stock market has entered a technologically advanced era, revamping the old model of trading. With the ceaseless increase in market capitalization, stock trading has become a center of investment for many financial investors. Many analysts and researchers have developed tools and techniques that predict stock price movements and help investors in proper decision-making. Advanced trading models enable researchers to predict the market using non-traditional textual data from social platforms. The application of advanced machine learning approaches such as text data analytics and ensemble methods have greatly increased the prediction accuracies. Meanwhile, the analysis and prediction of stock markets continue to be one of the most challenging research areas due to dynamic, erratic, and chaotic data. This study explains the systematics of machine learning-based approaches for stock market prediction based on the deployment of a generic framework. Findings from the last decade (2011–2021) were critically analyzed, having been retrieved from online digital libraries and databases like ACM digital library and Scopus. Furthermore, an extensive comparative analysis was carried out to identify the direction of significance. The study would be helpful for emerging researchers to understand the basics and advancements of this emerging area, and thus carry-on further research in promising directions.

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Section: Challenges and Open Issuesmentioning

confidence: 99%

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

et al. 2021

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“…However, if the technology itself does not serve its purpose, users would certainly not adopt it, despite its ease of use (Davis, 1989). Having said that, in the context of banking (Alt et al, 2021), and finance (Huang, Lee, & Lee, 2021) applications for instance, ‘ease of use’ is more relevant and significant in explaining adoption. Nonetheless, one cannot deny the importance of both variables in explaining adoption.…”

Section: Introductionmentioning

confidence: 99%

“…It is a psychological concept and serves as a ‘balancing act’ between others' interests vis a vis self‐interest (Goldsmith, 2015; Paul et al, 2022). In the case of service chatbots, social influence is indeed a significant variable that explains adoption in applications related to Tourism (Melián‐González et al, 2021), Health (Huang, Yang, & Huang, 2021), and Financial Services (Huang, Lee, & Lee, 2021).…”

Section: Introductionmentioning

confidence: 99%

“…Other similar studies have associated the performance expectancy variable with the PU variable in TAM. Thus, it may be affirmed that both these variables are critical in explaining the intent to use service chatbots for applications like finance (Huang, Yang, & Huang, 2021), banking (Alt et al, 2021), education (Almahri et al, 2020; Chatterjee & Bhattacharjee, 2020), health (Huang, Lee, & Lee, 2021), smart technologies (Pal, Arpnikanondt, Funilkul, & Razzaque, 2020), and transportation (Kuberkar & Singhal, 2020). However, since these variables are in line with the proposed variables from TAM, they are not individually hypothesized.…”

Section: Introductionmentioning

confidence: 99%

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Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Gopinath

Kasilingam

2023

Int J Consumer Studies

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The trend of using artificial intelligence based chatbots for service encounters has seen a significant spike in recent times. Many studies have attempted to explain the adoption of the technology for various service applications; this study synthesizes the literature through a meta‐analysis, for which data from 70 independent studies, with a combined sample size of 20,026 was considered, to understand the influencers of the attitude and intention to use chatbots for service encounters. In addition, this study also sought to understand the moderating role of the culture, the technological characteristics captured through the variables transactional/non‐transactional, and informative/entertaining of the hypothesized relationships. Meta structural equation modelling (meta‐analytic SEM) was used to analyse the data. The results indicate playfulness, attitude, usefulness, facilitating condition and social influence of the chatbots are the key factors in influencing user adoption. Further, technological characteristics and culture were found to significantly moderate certain relationships in the model. Practical and theoretical implications have also been discussed.

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“…Although various researchers have conducted a study on the intention to continue using FinTech (Daragmeh et al, 2021;Diana & Leon, 2020;Franque et al, 2021;Huang et al, 2021;Jung, 2017;Odoom & Kosiba, 2020;Oktavendi, 2020;Ryu, 2018;Shiau et al, 2020;Z. Wang et al, 2019).…”

Section: Introductionmentioning

confidence: 99%

Impacts Of Digital Technostress And Digital Technology Self-Efficacy On Intentions To Use Fintech In Indonesia

Putriani

Apriani²

2022

JRAK

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The purpose of this study is to see how digital technostress and digital technology self-efficacy (DTSE) affect Generation Z consumers' willingness to adopt FinTech in Indonesia. This study provides knowledge about the impact of technostress and the role of self-efficacy as a moderator on intention to use FinTech. This study uses a questionnaire survey with the criteria of respondents who have used FinTech and Generation Z (born 1993-2012) in Indonesia. A total of 122 respondents were analyzed by Structural Equation Modeling (SEM). The results of the study show that complexity and overload reduce the use of FinTech. In addition, DTSE increases their intention to use FinTech and can reduce the impact of technostress on intention to use FinTech. The results of the research are expected to be input for innovators and policymakers to make FinTech applications easier to use so that consumers will continue to use FinTech to support sustainable development.

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Toward a Unified Theory of Customer Continuance Model for Financial Technology Chatbots

Cited by 14 publications

References 67 publications

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Stock Market Prediction Using Machine Learning Techniques: A Decade Survey on Methodologies, Recent Developments, and Future Directions

Antecedents of intention to use chatbots in service encounters: A meta‐analytic review

Impacts Of Digital Technostress And Digital Technology Self-Efficacy On Intentions To Use Fintech In Indonesia

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