International Research Journal of Innovations in Engineering and Technology - IRJIET International Open Access, Monthly, Peer Reviewed, Reputed Journal ISSN (online): 2581-3048

Impact Factor (2025): 6.9

DOI Prefix: 10.47001/IRJIET

Factors that Contribute Positively to Improve the Performance of Innovation for Saudi Arabia

Abstract

This paper is an attempt to examine the determinants of innovation proxies by the annual score of Global Innovation Index (GII) in Saudi Arabia over the period from 2011 to 2018 by using two statistical approaches: Box–Jenkins (1970) approach and multiple regression approach. Moreover, based on the estimation results from the two mentioned approaches, we presented the forecasted values for GII score over the period from 2019 to 2023.The results proposed that infrastructure, market sophistication, and creative outputs are important factors that contributed positively towards improve the annual score of GII in Saudi Arabia, and this score will increase in the coming years.

Country : Saudi Arabia

1 Dr. Ahmad Alkhaleefah

  1. College of Engineering; Production, Manufacturing Management, Prince Sultan University, Saudi Arabia

IRJIET, Volume 4, Issue 6, June 2020 pp. 26-36

doi.org/10.47001/IRJIET/2020.406003

Full Paper
Download

References

  1. Abonazel, M. R. and Abd-Elftah, A. I. (2019). Forecasting Egyptian GDP Using ARIMA Models. Reports on Economics and Finance, 5(1), 35-47.
  2. Alkhaleefah, A. (2018). What To Do To Improve The International Saudi Innovation Rank/Score. Scopus, 9, 435-442.
  3. Blind, K. (2012). The influence of regulations on innovation: A quantitative assessment for OECD countries. Research Policy, 41(2), 391-400.‏
  4. Breusch, T. S. (1978). Testing for Autocorrelation in Dynamic Linear Models. Australian Economic Papers, 17, 334–355.
  5. Breusch, T.S. and A.R. Pagan. (1979). A simple test for heteroscedasticity and random coefficient variation. Econometrica, 47, 1287- 1294.
  6. Chatfield, C. (2016). The Analysis of Time Series: An Introduction. CRC Press.
  7. Ghazal, R., and Zulkhibri, M. (2015). Determinants of innovation outputs in developing countries: Evidence from panel data negative binomial approach. Journal of economic studies, 42(2), 237-260.‏
  8. Godfrey, L. G. (1978a). Testing for multiplicative heteroscedasticity. Journal of Econometrics, 8, 227–236.
  9. Godfrey, L. G. (1978b). Testing against general autoregressive and moving average error models when the regressors include lagged dependent variables. Econometrica, 46, 1293–1301.
  10. Jarque, Carlos M.; Bera, Anil K. (1980). Efficient tests for normality, homoscedasticity and serial independence of regression residuals. Economics Letters, 6 (3), 255–259.
  11. Khiari, M., & ben Rejeb, J. (2015). Determination of the Regional Impact on Innovation with an Ordinal Logit And a Multilevel Analysis. Procedia-Social and Behavioral Sciences, 195, 592-602.‏
  12. Lasagni, A. (2012). How can external relationships enhance innovation in SMEs? New Evidence for Europe. Journal of small business management, 50(2), 310-339.‏
  13. Nour, S. M. (2016). Overview of National Systems of Innovation in the Arab Countries. In Economic Systems of Innovation in the Arab Region, 91-165. Palgrave Macmillan, New York.‏
  14. Raghupathi, V., and Raghupathi, W. (2017). Innovation at Country-Level: Association between Economic Development and Patents. Journal of Innovation and Entrepreneurship, 6(1), 1-20.‏
  15. WUNSCH-VINCENT, S. A. C. H. A., and GUADAGNO, F. (2015). Benchmarking Innovation Outperformance at the Global and Country Levels. The Global Innovation Index 2015: Effective Innovation Policies for Development, 65-79.‏

Copyright @ 2026-2017 IRJIET. All Right Reserved.

Developed by Byzero Technologies