Submit a Manuscript
Publishing with us to make your research visible to the widest possible audience.
Department of Industrial Engineering and Management, National Taipei University of Technology, Taipei, Taiwan
To understand the factors influencing AI engine robot use, this study developed a conceptual model along the lines of previous research. The research model hypothesized that personal innovativeness in Information Technology (new software, new hardware, new AI engine, new chatbot, use new technology), perceived usefulness of Information Technology and perceived ease of use of Information Technology positively affect attitude toward AI engine robots, which in turn facilitates AI engine robot intention and AI engine robot use. By collecting data from 55 surveys from college Student’s respondents who have employed AI engine robots, we applied statistics to test the relationships in the model. Our findings demonstrate a positive effect of personal innovativeness in Information Technology, perceived usefulness of Information Technology and perceived ease of use of Information Technology on attitude toward AI engine robots. In addition, attitude toward AI engine robots has a positive effect on AI engine robot intention and AI engine robot use. Students use the AI engine to score the natural language description of the copywriting. Future research in the field of natural language processing and natural language understanding, using several AI engines for mathematical operations and applications. Quantitative research on natural language processing and natural language understanding. Used to solve more complex NLP and NLU and sentiment analysis problems.
NLP, NLU, Sentiment Analysis, AI Engine Robot, Information Technology
Chin-Liang Hung, Chui-Yu Chiu. (2023). Exploring Factors Influencing AI Sentiment-Analysis Engine Robot Use - Surveying Students in Social Science College. American Journal of Artificial Intelligence, 7(1), 1-5. https://doi.org/10.11648/j.ajai.20230701.11
Copyright © 2023 Authors retain the copyright of this article.
This article is an open access article distributed under the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
|1.||Agarwal, R. and Prasad, J. (1998). A Conceptual and Operational Definition of Personal Innovativeness in the Domain of Information Technology”, Information Systems Research, Vol. 9 No. 2, pp. 204–215.|
|2.||Armstrong, G., and Kotler, P. (2000), Marketing, Paper presented at the 5th ed., Prentice-Hall, Englewood Cliffs, 153-154.|
|3.||Davis, F. D. (1989), “Perceived usefulness, perceived ease of use, and user acceptance of information technology”, MIS Quarterly, Vol. 13 No. 3, pp. 319–340.|
|4.||DeLone, W. H. and Mclean, E. R. (1992), Information system success: The quest for the dependent variable. Information System Research, Vol 3, pp. 60-95.|
|5.||https://www.precedenceresearch.com/ Published: August 2022.|
|6.||Grandom, E., and Mykytyn, P. (2004), “Theory-based instrumentation to measure the intention to use electronic commerce in small and medium sized businesses”, Journal of Computer Information Systems, Vol. 44, pp. 44-57.|
|7.||Haque, A., Sadeghzadeh, J., and Khatibi, A. (2006), “Identifying potentiality online sales in Malaysia: A study on customer relationships online shopping”, Journal of Applied Business Research, Vol. 22, pp. 119-130.|
|8.||Hopkins, N., Sylvester, A. and Tate, M. (2013), Motivations For BYOD: An Investigation Of The Contents Of A 21st Century School Bag, ECIS 2013 Completed Research.|
|9.||Lewis, W., Agarwal, R. and Sambamurthy, V. (2003), “Sources of Influence on Beliefs about Information Technology Use: An Empirical Study of Knowledge Workers”, MIS Quarterly, Vol. 27 No. 4, pp. 657–678.|
|10.||Nüesch, R., Alt, R., & Puschmann, T. (2015). Hybrid customer interaction. Business & Information Systems Engineering, 57 (1), 73-82.|
|11.||Nunnally, J. C., and Bernstein, I. H. (1994). Psychometric theory (3rd ed.). New York: McGraw-Hill.|
|12.||Schiffman, L. G., and Kanuk, L. L. (2000). Consumer behavior. Wisconsin: Prentice Hall.|
|13.||Taylor, S. and Todd, P. A. (1995), “Decomposition of cross effects in the theory of planned behavior: A study of consumer adoption intentions”, International Journal of Research in Marketing, Vol. 12, pp. 137-155.|
|14.||Udell, J. G. (1965), “Can Attitude Measurement Predict Consumer Behaviour”. Journal of Marketing. Vol. 29, pp. 46-50.|
|15.||Vantomme, D., Geuens, M., De Houwer, J. and De Pelsmacker, P. (2005), “Implicit attitudes toward green consumer behaviour”. Psychologica belgica, Vol. 45, pp. 217-239.|
|16.||Weeger, A. and Gewald, H. (2014), “Factors Influencing Future Employees Decision-Making to Participate in a BYOD Program: Does Risk Matter?” Proceedings of the European Conference on Information Systems (ECIS), Tel-Aviv, Israel.|
|17.||Hair, Jr. J. F., Anderson, R. E., Tatham, R, L. & Black, W. C. (1998). Multivariate data analysis (5th ed.). Englewood Cliffs, NJ: Prentice Hall.|