iosr-jmca   Volume-9 ~ Issue-1 ~ Jan. - Feb. 2022

ABSTRACT: Mobile payment is becoming increasingly popular among consumers, with convenient and more or less risky means of identification and authentication. This study aims to investigate the attitudinal and behavioural preferences for two mobile payment application technologies, namely fingerprint scanning and QR-code scanning. Specifically, a comparison is made of consumers from cultures with different levels of uncertainty avoidance. For this purpose, Asian and European consumers from China (N = 70) and the Netherlands (N = 110) participated in an online survey. The findings revealed that both cultural groups regularly used mobile payment technology, whereby they clearly preferred.......

Key wards: Mobile payment, trust, uncertainty avoidance, cross-cultural, Asia, Europe

[1]. Babin, B. J., Darden, W. R., & Griffin, M. (1994). Work and/or fun: measuring hedonic and utilitarian shopping value. Journal of Consumer Research, 20(4), 644-656. Doi: 10.1086/209376
[2]. Batra, R., & Ahtola, O. T. (1991). Measuring the hedonic and utilitarian sources of consumer attitudes. Marketing Letters, 2(2), 159-170.
[3]. Boden, J., Maier, E., & Wilken, R. (2020). The effect of credit card versus mobile payment on convenience and consumers' willingness to pay. Journal of Retailing and Consumer Services, 52. Doi: 10.1016/j.jretconser.2019.101910
[4]. Chandra, S., Srivastava, S. C., & Theng, Y. L. (2010). Evaluating the role of trust in consumer adoption of mobile payment systems: An empirical analysis. Communications of the Association for Information Systems, 27(1), 561-588. Doi: 10.17705/1CAIS.02729
[5]. Cao, X., Yu, L., Liu, Z., Gong, M., & Adeel, L. (2018). Understanding mobile payment users' continuance intention: A trust transfer perspective. Internet Research, 28(3), 456–476. Doi: 10.1108/IntR-11-2016-0359.

ABSTRACT: Countries in Africa face an array of health care financing problems that leave their health systems far from achieving the objectives of good health status, equity, efficiency, acceptability, and sustainability. The main problem is simply a shortage of government budgetary resources for health care relative to increasing demand and need for care. One manifestation of the budgetary shortfall is deterioration in the quality and effectiveness of publicly provided health services (Barnum and Saxenian 1995). Other challenges fan from lack of interoperability in health care systems. Rising costs, reduced Medicare reimbursements, increased pressure to improve the patient experience, and a shift to value-based care all mean that.......

[1]. Ajzen, I. (1991). The theory of planned behavior. Organizational Behavior and Human Decision Processes, 50(2), 179-211
[2]. Ajzen, I. (1985). From intentions to actions: a theory of planned behaviour, in Kuhl, J. and Beckmann, J. (Eds), Action-Control: From Cognition to Behaviour, Springer- Verlag, Heidelberg, pp. 11-39.
[3]. Aker, J. C. (2008), "Does Digital Divide or Provide? The Impact of Cell Phones on Grain Markets in Niger, Mimeo, University of California, Berkeley.
[4]. Anderson (1997). Clearing the way for physicians' use of clinical information systems.Commun ACM.
[5]. Bock, Carnahan, Fenves, Gruninger, Kashyap, Lide, Nell, Raman and Sriram, (2005)..

ABSTRACT: This study proposes a test of Emotional Quotient (EQ) of Unified Tertiary Matriculation Examination(UTME) candidates by Universities they have applied to, as an alternative to duplicate Intelligent Quotient test (Post UTME) presently practiced. The proposed assessment system was tested by designing a questionnaire that asks EQ questions and uses students' responses on whether they gained Merit or Supplementary admission and tagged academic ability. EQ ordinal questions were summed and transformed into categorical questions that are used to derive interaction with independent variables. Performance of EQ as Dependent, while demographic information was used as Independent variable, was derived using classification tree. Similarly, association with students' admission type as the dependent variable while EQ items and demographic data is independent variable was also derived. ......

Keywords: Classification Tree, Likert scale analysis, EQ Models, Assessment PUTME, UTME, JAMB, University, Admission, Emotional Quotient, Intelligent Quotient.

[1]. Adel, Z., Mahdi M. (2010). The role of emotional intelligence in predicting students' academic achievement in distance education system, culture-process-and-phenomenon-synchronic-and-diachronic particular-and-universal.pdf 12 Trait emotional intelligence and its links to university performance: An examination https://doi.org/10.1016/j.paid.2012.11.013 Accessed 12/3/2021
[2]. Alison, P. S. (2016). What is !Q- and how much does it matter"
https://www.sciencenewsforstudents.org/article/what-iq-and-how-much-does-it-matter Accessed 10/2/21
[3]. Anthony I. A., Oluwaseyi D. S. (2020) Recreational drug use among Nigerian university students: Prevalence, correlates and frequency of use; https://doi.org/10.1371/journal.pone.0232964 Accessed 10/2/21
[4]. Bar-On, R. (2006). The Bar-On model of emotional-social intelligence (ESI). Psicothema, 18(Suppl), 13–25
[5]. Ellen B. B., Dennis N. (2006) Intelligence (IQ) Testing Pediatr Rev (2006) 27 (11): 403–408. https://doi.org/10.1542/pir.27.11.403 Accessed 10/2/21

ABSTRACT: The spread of hatred that was formerly limited to verbal communications has rapidly moved over the Internet. Social media and community forums that allow people to discuss and express their opinions are becoming platforms for the spreading of hate messages. Many countries have developed laws to avoid online hate speech. They hold the companies that run the social media responsible for their failure to eliminate hate speech. But as online content continues to grow, so does the spread of hate speech However, manual analysis of hate speech on online platforms is infeasible due to the huge amount of data as it is expensive and time consuming. Thus, it is important to automatically process the online user contents to detect.......

Keywords: classification; hate speech; feature extraction, algorithm, supervised learning

[1]. AlMessabi,K.,Aldwairi,M.,AlYousif,A.,Thoban,A.,Belqasmi,F.,2018. Malware detection using dns records and domain name features", in: International Conference on Future Networks and Distributed Systems (ICFNDS), ACM. URL: https://doi.org/10.1145/3231053. 3231082.
[2]. A Erlich, DF Jung, JD Long, C McIntosh, 2018: The double-edged sword of mobilizing citizens via mobile phone in developing countries Development Engineering 3, 34-46
[3]. Balmas,M.,(2014). When fake news becomes real: Combined exposure to multiple news sources and political attitudes of inefficacy, alienation, and cynicism. Communication Research 41, 430–454. doi:10.1177/0093650212453600.
[4]. Brown-Sica, Margaret; Beall, Jeffrey (2008). "Library 2.0 and the Problem of Hate Speech". Electronic Journal of Academic and Special Librarianship. 9 (2). Retrieved 22 June 2021.
[5]. Burnap, P. and M.L. Williams, (2016) US and them: identifying cyber hate on Twitter across multiple protected characteristics. EPJ Data Science, 2016. 5(1).

ABSTRACT: An interactive chatbot enabled service for result information system was built in response to the challenges students had dealing with the existing result information system for students' retrieval. The proposed system was created using a natural language processing model, CSS and Tailwind for graphical design, HTML for page alignment, and the flask framework to enable web-based chatbot development easier. According to the results, the designed system received an overall score of 87.35 percent comprehension, 91 percent functionality, 76.63 percent speed, 80 percent interoperability, 88.05 percent engagement, and 87.23 percent scalability.

Keywords: NLP, Chatbot, Information system, Artificial Intelligence, Agents

[1]. Ardiana, D. P. Y., Joni, I. D. M. A. B., and Udayana, I. P. A. E. D. (2020). Mobile based chatbot application for HIV / AIDS counseling using artificial intelligence markup language approach Mobile based chatbot application for HIV / AIDS counseling using artificial intelligence markup language approach. International Journal of Physics, 1469, 2–8. https:// doi.org/10.1088/1742-6596/1469/1/012041
[2]. Arora, S., Batra, K., and Singh, S. (2012). Dialogue Systemn: A Brief Review. 3(2), 2–5.
[3]. Bushra, K., and Nadesh, R. (2020). Design and Development of Diagnostic Chabot for supporting Primary Health Care Systems. Nternational Conference on Computational Intelligence and Da- ta Science, 167, 75–84. https://doi.org/10.1016/j.procs.2020.03.184
[4]. Candela, E. (2018). Consumers' perception and attitude to wards chatbots' adoption: A focus on the Italian (Master's Thesis, Aalbor University; Vol. 1). Retrieved from https:// projekter.aau.dk/projekter/files/281244069/IMthesis
[5]. Comendador, B. E. V, Francisco, B. M. B., Medenilla, J. S., Sharleen, M. T., and Serac, T. B. E. (2015). Pharmabot : A Pediatric Generic Medicine Consultant Chatbot. 3(2), 137–140. https:// doi.org/10.12720/joace.3.2.137-140