IS FINTECH A SAVIOR IN COVID19 EVIDENCE FROM AN EMERGING ECONOMY

http://dx.doi.org/10.31703/gssr.2022(VII-II).29      10.31703/gssr.2022(VII-II).29      Published : Jun 2
Authored by : Khurram Ashfaq , Adil Riaz , Usman Haider

29 Pages : 285-294

References

  • Fulk, J., SChmitz, J., & Steinfield, C. (1990). A Social Influence Model of Technology use. Organizations and Communication Technology, 117–140. https://doi.org/10.4135/9781483325385.n6
  • Hwang, Y., & Kim, D. J. (2007). Customer self- service systems: The effects of perceived Web quality with service contents on enjoyment, anxiety, and e-trust. Decision Support Systems, 43(3), 746–760. https://doi.org/10.1016/j.dss.2006.12.008
  • Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1–9. https://doi.org/10.1016/j.im.2007.03.005
  • Ituma, A. I., Riaz, A., & Ali, M. H. (2021). EXAMINATION OF DIGITAL AND NON-DIGITAL FACTORS ON PERCEPTION OF MOBILE BANKING CUSTOMERS: A CASE OF DEVELOPING ECONOMY. DECEMBER, 37(04), 388– 399. https://doi.org/10.51380/gujr-37-04-02
  • Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences, 28(2), 357–389. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
  • Liu, D., & Tu, W. (2021). Factors influencing consumers’ adoptions of biometric recognition payment devices: combination of initial trust and UTAUT model. International Journal of Mobile Communications, 19(3), 345. https://doi.org/10.1504/ijmc.2021.114324
  • Liu, M., Chu, R., Wong, I. A., Angel Zúñiga, M., Meng, Y., & Pang, C. (2012). Exploring the relationship among affective loyalty, perceived benefits, attitude, and intention touse co‐branded products. Asia Pacific Journal of Marketing and Logistics, 24(4), 561–582. https://doi.org/10.1108/13555851211259025
  • Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106–120. https://doi.org/10.1108/09685220510589299
  • Manrai, R., & Gupta, K. P. (2020). Integrating utaut with trust and perceived benefits to explain user adoption of mobile payments. In Strategic system assurance and business analytics , Springer. 109-121
  • Pai, C. K., Wang, T. W., Chen, S. H., & Cai, K. Y. (2018). Empirical study on Chinese tourists’ perceived trust and intention to use biometric technology. Asia Pacific Journal of Tourism Research, 23(9), 880–895. https://doi.org/10.1080/10941665.2018.1499 544
  • Shen, A. X. L., Cheung, C. M. K., Lee, M. K. O., & Chen, H. (2009). How social influence affects we-intention to use instant messaging:
  • The moderating effect of usage experience. Information Systems Frontiers, 13(2), 157– 169. https://doi.org/10.1007/s10796-009-9193-9
  • Tan, P. J. B. (2013). Students’ adoptions and attitudes towards electronic placement tests: A UTAUT analysis. American Journal of Computer Technology and Application, 1(1), 14-23.
  • Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x

References

  • Fulk, J., SChmitz, J., & Steinfield, C. (1990). A Social Influence Model of Technology use. Organizations and Communication Technology, 117–140. https://doi.org/10.4135/9781483325385.n6
  • Hwang, Y., & Kim, D. J. (2007). Customer self- service systems: The effects of perceived Web quality with service contents on enjoyment, anxiety, and e-trust. Decision Support Systems, 43(3), 746–760. https://doi.org/10.1016/j.dss.2006.12.008
  • Im, I., Kim, Y., & Han, H. J. (2008). The effects of perceived risk and technology type on users’ acceptance of technologies. Information & Management, 45(1), 1–9. https://doi.org/10.1016/j.im.2007.03.005
  • Ituma, A. I., Riaz, A., & Ali, M. H. (2021). EXAMINATION OF DIGITAL AND NON-DIGITAL FACTORS ON PERCEPTION OF MOBILE BANKING CUSTOMERS: A CASE OF DEVELOPING ECONOMY. DECEMBER, 37(04), 388– 399. https://doi.org/10.51380/gujr-37-04-02
  • Jackson, C. M., Chow, S., & Leitch, R. A. (1997). Toward an Understanding of the Behavioral Intention to Use an Information System. Decision Sciences, 28(2), 357–389. https://doi.org/10.1111/j.1540-5915.1997.tb01315.x
  • Liu, D., & Tu, W. (2021). Factors influencing consumers’ adoptions of biometric recognition payment devices: combination of initial trust and UTAUT model. International Journal of Mobile Communications, 19(3), 345. https://doi.org/10.1504/ijmc.2021.114324
  • Liu, M., Chu, R., Wong, I. A., Angel Zúñiga, M., Meng, Y., & Pang, C. (2012). Exploring the relationship among affective loyalty, perceived benefits, attitude, and intention touse co‐branded products. Asia Pacific Journal of Marketing and Logistics, 24(4), 561–582. https://doi.org/10.1108/13555851211259025
  • Lu, H., Hsu, C., & Hsu, H. (2005). An empirical study of the effect of perceived risk upon intention to use online applications. Information Management & Computer Security, 13(2), 106–120. https://doi.org/10.1108/09685220510589299
  • Manrai, R., & Gupta, K. P. (2020). Integrating utaut with trust and perceived benefits to explain user adoption of mobile payments. In Strategic system assurance and business analytics , Springer. 109-121
  • Pai, C. K., Wang, T. W., Chen, S. H., & Cai, K. Y. (2018). Empirical study on Chinese tourists’ perceived trust and intention to use biometric technology. Asia Pacific Journal of Tourism Research, 23(9), 880–895. https://doi.org/10.1080/10941665.2018.1499 544
  • Shen, A. X. L., Cheung, C. M. K., Lee, M. K. O., & Chen, H. (2009). How social influence affects we-intention to use instant messaging:
  • The moderating effect of usage experience. Information Systems Frontiers, 13(2), 157– 169. https://doi.org/10.1007/s10796-009-9193-9
  • Tan, P. J. B. (2013). Students’ adoptions and attitudes towards electronic placement tests: A UTAUT analysis. American Journal of Computer Technology and Application, 1(1), 14-23.
  • Venkatesh, V., Thong, J. Y. L., Chan, F. K. Y., Hu, P. J. H., & Brown, S. A. (2011). Extending the two-stage information systems continuance model: incorporating UTAUT predictors and the role of context. Information Systems Journal, 21(6), 527–555. https://doi.org/10.1111/j.1365-2575.2011.00373.x

Cite this article

APA

CHICAGO

    CHICAGO : Ashfaq, Khurram, Adil Riaz, and Usman Haider. 2022. "Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy." Global Social Sciences Review, VII (II): 285-294 doi: 10.31703/gssr.2022(VII-II).29

HARVARD

    HARVARD : ASHFAQ, K., RIAZ, A. & HAIDER, U. 2022. Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy. Global Social Sciences Review, VII, 285-294.

MHRA

    MHRA : Ashfaq, Khurram, Adil Riaz, and Usman Haider. 2022. "Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy." Global Social Sciences Review, VII: 285-294

MLA

    MLA : Ashfaq, Khurram, Adil Riaz, and Usman Haider. "Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy." Global Social Sciences Review, VII.II (2022): 285-294 Print.

OXFORD

    OXFORD : Ashfaq, Khurram, Riaz, Adil, and Haider, Usman (2022), "Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy", Global Social Sciences Review, VII (II), 285-294

TURABIAN

    TURABIAN : Ashfaq, Khurram, Adil Riaz, and Usman Haider. "Is FinTech a Savior in COVID-19? Evidence from an Emerging Economy." Global Social Sciences Review VII, no. II (2022): 285-294. https://doi.org/10.31703/gssr.2022(VII-II).29