International journal of

ADVANCED AND APPLIED SCIENCES

EISSN: 2313-3724, Print ISSN:2313-626X

Frequency: 12

line decor
  
line decor

Volume 4, Issue 11 (November 2017), Pages: 35-42

----------------------------------------------

Review Paper

Title: User acceptance towards optical character recognition for international call Apps: Comparing the accuracy of the prediction by neural networks and SPSS equation

Author(s): Faris Ahmed Alshammari, Ahmad Suhaimi Baharudin, Kamal Karkonasasi *

Affiliation(s):

School of Computer Science, USM, 11800, Sungai Dua, Pulau Penang, Malaysia

https://doi.org/10.21833/ijaas.2017.011.006

Full Text - PDF          XML

Abstract:

In the field of artificial intelligence and pattern recognition, the new innovative ideology called optical character recognition (OCR) has been prominent and most successful. In the past 50 years, the idea of machine reading has developed from the stage of a dream to the stage of reality and certainty. There are many systems variety of applications in existence that are commercially based for the operation and application of OCR. The incessant problem of a series of stages before making a successful international call is a beacon for urgent attention hence the desire for the business proposal. This idea makes use of OCR as a means of making faster international mobile apps card calls without going through the usual series of stages before the call can get through to the foreign country. The OCR was used to scan the serial digits of the prepaid card and immediately transfer it for the immediate call. The idea was found to be more appropriate, user friendly and faster than the early traditional system of making international calls. 

© 2017 The Authors. Published by IASE.

This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

Keywords: OCR, Application of OCR, Accuracy, Neural networks

Article History: Received 13 April 2017, Received in revised form 7 September 2017, Accepted 18 September 2017

Digital Object Identifier: 

https://doi.org/10.21833/ijaas.2017.011.006

Citation:

Alshammari FA, Baharudin AS, and Karkonasasi K (2017). User acceptance towards optical character recognition for international call Apps: Comparing the accuracy of the prediction by neural networks and SPSS equation. International Journal of Advanced and Applied Sciences, 4(11): 35-42

Permanent Link:

http://www.science-gate.com/IJAAS/V4I11/Alshammari.html

----------------------------------------------

References (31)

  1. Aldas-Manzano J, Ruiz-Mafe C, and Sanz-Blas S (2009). Exploring individual personality factors as drivers of m-shopping acceptance. Industrial Management and Data Systems, 109(6): 739–757. https://doi.org/10.1108/02635570910968018 
  2. Androulidakis N and Androulidakis I (2005). Perspectives of mobile advertising in Greek market. In the International Conference on Mobile Business, IEEE, Sydney, Australia: 441-444. https://doi.org/10.1109/ICMB.2005.78 
  3. Bhattacherjee A (2002). Individual trust in online firms: Scale development and initial test. Journal of Management Information Systems, 19(1): 211–241. https://doi.org/10.1080/07421222.2002.11045715 
  4. Chen J and Tong L (2003). Analysis of mobile phone's innovative will and leading customers. Science Research Management, 24(3): 25–31.     
  5. Chen L (2008). A model of consumer acceptance of mobile payment. International Journal of Mobile Communications, 6(1): 32–52. https://doi.org/10.1504/IJMC.2008.015997 
  6. Chen Q, Chen HM, and Kazman R (2007). Investigating antecedents of technology acceptance of initial eCRM users beyond generation X and the role of self-construal. Electronic Commerce Research, 7(3): 315-339. https://doi.org/10.1007/s10660-007-9009-2 
  7. Cho DY, Kwon HJ, and Lee HY (2007). Analysis of trust in internet and mobile commerce adoption. In the 40th Annual Hawaii International Conference on System Sciences, IEEE, Waikoloa, USA: 50-50. https://doi.org/10.1109/HICSS.2007.76 
  8. Davis D (1989). Perceived usefulness, perceived ease of use and user acceptance of information technology. Management Information Systems Quarterly, 13(3): 319–340. https://doi.org/10.2307/249008 
  9. Drury G (2008). Social media: Should marketers engage and how can it be done effectively?. Journal of Direct, Data and Digital Marketing Practice, 9(3): 274–277. https://doi.org/10.1057/palgrave.dddmp.4350096 
  10. Fishbein M and Ajzen I (1975). Belief, attitude, intention and behavior: An introduction to theory and research. Addison-Wesley, Boston, USA.     
  11. Flynn L and Goldsmith R (1993). A validation of the Goldsmith and Hofacker innovativeness scale. Educational and Psychological Measurement, 53(4): 1105–1116. https://doi.org/10.1177/0013164493053004023 
  12. Hittleman DR and Simon AJ (1997). Interpreting educational research: An introduction for consumers of research. Prentice-Hall, Inc., Upper Saddle River, USA.     
  13. Im I, Hong S, and Kang MS (2011). An international comparison of technology adoption: Testing the UTAUT model. Information and Management, 48(1): 1-8. https://doi.org/10.1016/j.im.2010.09.001 
  14. Kuo Y and Yen S (2009). Towards an understanding of the behavioral intention to use 3G mobile value-added services. Computers in Human Behavior, 25(1): 103–110. https://doi.org/10.1016/j.chb.2008.07.007 
  15. Lai VS and Li H (2005). Technology acceptance model for internet banking: an invariance analysis. Information and Management, 42(2): 373-386. https://doi.org/10.1016/j.im.2004.01.007 
  16. Lam S, Chiang J, and Parasuraman A (2008). The effects of the dimensions of technology readiness on technology acceptance: An empirical analysis. Journal of Interactive Marketing, 22(4): 19–39. https://doi.org/10.1002/dir.20119 
  17. Lee MC (2009). Factors influencing the adoption of Internet banking: An integration of TAM and TPB with perceived risk and perceived benefit. Electronic Commerce Research and Applications, 8(3): 130–141. https://doi.org/10.1016/j.elerap.2008.11.006 
  18. Lu J, Yao J, and Yu C (2005). Personal innovativeness, social influences and adoption of wireless internet services via mobile technology. Journal of Strategic Information Systems, 14(3): 245–268. https://doi.org/10.1016/j.jsis.2005.07.003 
  19. Min Q, Ji S, and Qu G (2008). Mobile commerce user acceptance study in China: A revised UTAUT model. Tsinghua Science and Technology, 13(3): 257–264. https://doi.org/10.1016/S1007-0214(08)70042-7 
  20. Minhyung K (2010). The mobile big bang. SERI Quarterly, 3(4): 79–85.     
  21. Misra S and Wickamasinghe N (2004). Security of a mobile transaction. Electronic Commerce Research, 4(4): 359–372. https://doi.org/10.1023/B:ELEC.0000037082.39182.3a 
  22. Mun YY, Jackson JD, Park JS, and Probst JC (2006). Understanding information technology acceptance by individual professionals: Toward an integrative view. Information and Management, 43(3): 350-363. https://doi.org/10.1016/j.im.2005.08.006 
  23. Pavlou P (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7(3): 101–134.     
  24. Polančič G, Heričko M, and Rozman I (2010). An empirical examination of application frameworks success based on technology acceptance model. Journal of Systems and Software, 83(4): 574-584. https://doi.org/10.1016/j.jss.2009.10.036 
  25. Qi J, Li L, Li Y, and Shu H (2009). An extension of technology acceptance model: Analysis of the adoption of mobile data services in China. Systems Research and Behavioral Science, 26(3): 391–407. https://doi.org/10.1002/sres.964 
  26. Sulaiman A, Jaafar NI, and Mohezar S (2006). An overview of mobile banking adoption among the urban community. International Journal of Mobile Communications, 5(2): 157-168. https://doi.org/10.1504/IJMC.2007.011814 
  27. Tsu Wei T, Marthandan G, Yee-Loong Chong A, Ooi KB, and Arumugam S (2009). What drives Malaysian m-commerce adoption? An empirical analysis. Industrial Management and Data Systems, 109(3): 370-388. https://doi.org/10.1108/02635570910939399 
  28. Walczuch R, Lemmink J, and Streukens S (2007). The effect of service employees' technology readiness on technology acceptance. Information and Management, 44(2): 206–215. https://doi.org/10.1016/j.im.2006.12.005 
  29. Wang C, Lo S, and Fang W (2008). Extending the technology acceptance model to mobile telecommunication innovation: The existence of network externalities. Journal of Consumer Behaviour, 7(2): 101–110. https://doi.org/10.1002/cb.240 
  30. Wu J and Wang S (2005). What drives mobile commerce? An empirical evaluation of the revised technology acceptance model. Information and Management, 42(5): 719–729. https://doi.org/10.1016/j.im.2004.07.001 
  31. Zarmpou T, Saprikis V, Markos A, and Vlachopoulou M (2012). Modeling users' acceptance of mobile services. Electronic Commerce Research, 12(2): 225-248. https://doi.org/10.1007/s10660-012-9092-x