Skip to main content
Log in

Structural equation model with PLS path modeling for an integrated system of publicly funded basic research

  • Published:
Scientometrics Aims and scope Submit manuscript

Abstract

This study develops and tests an integrated conceptual model of basic research evaluation from a varying perspective. The main objective is to obtain a more complete understanding of the external factors affecting the publicly fund basic research in a country. Structural Equation Modeling (SEM) with Partial Least Squares (PLS) is used to test the conceptual model with empirical data collected from WCY (World Competitiveness Yearbook) and ESI (Essential Science Indicators) database. Interrelationships among the research output and outcome, together with three external factors (resource, impetus, accumulative advantage) have been successfully explored and the conceptual model of journal evaluation has been examined.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Similar content being viewed by others

References

  • Adams, J. D. (1990), Fundamental stocks of knowledge and productivity growth, Journal of Political Economy, 98(4): 673–702.

    Article  Google Scholar 

  • Adams, J. D. (1996), Measuring science: An exploration, Proceedings of the National Academy of Sciences of the United States of America, 93: 2664–2670.

    Article  Google Scholar 

  • Aghion, P., Howitt, P. (1992), A model of growth through creative destruction, Econometrica, 60(2): 323–351.

    Article  MATH  Google Scholar 

  • Allison, P. D., Stewart, J. A. (1974), Productivity differences among scientists: Evidence for accumulative advantage, American Sociological Review, 39: 596–606.

    Article  Google Scholar 

  • Allison, P. D., Long, J. S., Krauz, T. K. (1982), Cumulative advantage and inequality in science, American Sociological Review, 47(5): 615–625.

    Article  Google Scholar 

  • Amato, S., Esposito, V.V., Tenenhaus, M., A Global Goodness-of-Fit Index for PLS Structural Equation Modeling. Oral Communication to PLS Club, HEC School of Management, France, March 24, 2004.

    Google Scholar 

  • Bollen, K. A., Structural Equations with Latent Variables. New York: Wiley, 1989.

    MATH  Google Scholar 

  • Bonitz, M., Bruckner, E., Scharnhorst, A. (1997), Characteristics and impact of the Matthew effect for countries, Scientometrics, 40(3): 407–422.

    Article  Google Scholar 

  • Chubin, D. E., Hackett, E. J., Peerless Science: Peer Review and U.S. Science Policy. New York: State University of New York Press, 1990.

    Google Scholar 

  • Chaves, C.V., Moro, S. (2007), Investigating the interaction and mutual dependence between science and technology, Research Policy, 36(8): 1204–1220.

    Article  Google Scholar 

  • Cole, J. R., Cole, S., Social Stratification in Science. Chicago: University of Chicago Press, 1973.

    Google Scholar 

  • Commission of the European Communities. (2005), European Innovation Scoreboard 2005.

  • Cozzens, S. E., Literature-based Data in Research Evaluation: A Manager’s Guide to Bibliometrics. Report to the National Science Foundation, Washington, DC., 1989.

  • Cozzens, S. E. (1997), The knowledge pool: Measurement challenges in evaluating fundamental research programs, Evaluation and Program Planning, 20(1): 77–89.

    Article  Google Scholar 

  • David, P., Mowery, D., Steinmuller, W. (1992), Analyzing the economic payoffs from basic research, Economics of Innovation and New Technology, 2(1): 73–90.

    Article  Google Scholar 

  • Dunn, S. C., Serker, R. F., Waller, M. A. (1994), Latent variables in business logistics research: Scale development and validation, Journal of Business Logistics, 15(2): 145–172.

    Google Scholar 

  • Elkana, Y., Lederberg, J., Merton, R. K., Thackray, A., Zuckerman, H., Toward a Metric of Science: The Advent of Science Indicators, New York: Wiley, 1978.

    Google Scholar 

  • Evenson, R. E., Kislev, Y. (1975), Agricultural research and productivity, Journal of Economic Literature, 14(4): 1342–1343.

    Google Scholar 

  • Fornell, C. R., Larcker, D. F. (1981), Structural equation models with unobservable variables and measurement error, Journal of Marketing Research, 18: 39–50.

    Article  Google Scholar 

  • Garver, M. S., Mentzer, J. T. (1999), Logistics research methods: Employing structural equation modeling to test for construct validity, Journal of Business Logistics, 20(1): 33–58.

    Google Scholar 

  • Gerbing, D. W., Anderson, J. C. (1988), An updated paradigm for scale development incorporating unidimensionality and its assessment, Journal of Marketing Research, 25: 186–192.

    Article  Google Scholar 

  • Griliches, Z. (1964), Research expenditures, education and the aggregate agricultural production function, American Economic Review, 54(6): 961–974.

    Google Scholar 

  • Griliches, Z., R&D and Productivity, In: Paul Stoneman (ed.) Handbook of the Economics of Innovation and Technological Change. Oxford: Blackwell, 1995.

    Google Scholar 

  • Guellec, D., Van Pottelsberghe De La Potterie, B. (2004), From R&D to productivity growth: Do the institutional settings and the source of funds of R&D matter? Oxford Bulletin of Economics and Statistics, 66(3): 353–378.

    Article  Google Scholar 

  • Hagstrom, W. O. (1971), Inputs, outputs, and the prestige of university science departments, Sociology of Education, 44: 375-397.

  • Hair, J., Anderson, R., Tatham, R., Black, W., Multivariate Data Analysis (5th ed.), New York: Prentice-Hall, 1998.

    Google Scholar 

  • Huffman, W. E., Evenson, R. E., Science for Agriculture, Ames, IA: Iowa State University Press, 1994.

    Google Scholar 

  • IMD (2000–2004), IMD World Competitiveness Yearbook. Lausanne: IMD.

    Google Scholar 

  • International Bank for Reconstruction and Development (IBRD), World Development Indicators, Washington, DC: World Bank, 1999.

    Google Scholar 

  • Jöreskog, K. G. (1970), A general method for analysis of covariance structure, Biometrika, 57: 239–251.

    MATH  MathSciNet  Google Scholar 

  • Keith, B., Babchuk, N. (1998), The quest for institutional recognition: a longitudinal analysis of scholarly productivity and academic prestige among sociology departments, Social Forces, 76(4): 1495–1533.

    Article  Google Scholar 

  • Koufteros, X. A. (1999), Testing a model of pull production: A paradigm for manufacturing research using structural equation modelling, Journal of Operations Management, 17: 467–488.

    Article  Google Scholar 

  • Long, S., Confirmatory Factor Analysis: A Preface to LISREL. London: Sage Publications, 1983.

    Google Scholar 

  • Lucas, R. E. (1988), On the mechanics of economic development, Journal of Monetary Economics, 22: 3–42.

    Article  Google Scholar 

  • Martin, B. R. (1996), The use of multiple indicators in the assessment of basic research, Scientometrics, 36(3): 343–362.

    Article  Google Scholar 

  • Martin, B. R., Skea, J. E. F., Academic Research Performance Indicators: An Assessment of the Possibilities. Brighton: Science Policy Research Unit, 1992.

    Google Scholar 

  • Mansfield, E. (1980), Basic Research and Productivity Increase in manufacturing, American Economic Review, 70(5): 863–873.

    Google Scholar 

  • Mansfield, E. (1991), Academic research and industrial innovation, Research Policy, 20: 1–20.

    Article  Google Scholar 

  • Mansfield, E. (1992), Academic research and industrial innovation: A further note, Research Policy, 21: 295–296.

    Article  Google Scholar 

  • Merton, R. K. (1968), The Matthew effect in science, Science, 159(3810): 56–63.

    Article  Google Scholar 

  • Merton, R. K. (1988), The Matthew effect II, ISIS, 79: 606–623.

    Article  Google Scholar 

  • Narin, F., Evaluative Bibliometrics: The Use of Publication and Citation Analysis in the Evaluation of Scientific Activity. Cherry Hill, NJ: Computer Horizons, 1976.

    Google Scholar 

  • Narin, F., Olivastro, D., Stevens, K. (1994), Bibliometrics theory, practice and problems, Evaluation Review, 18(1): 65–76.

    Article  Google Scholar 

  • OECD, 2005. Proposed Guidelines for Collecting and Integrating Technological Innovation Data, OSLO Manual (Organisation for Economic Cooperation and Development, Paris).

  • Phillimore, A. J. (1989), University research performance indicators in practice: The University Grants Committee’s evaluation of British universities, Research Policy, 18: 255–271.

    Article  Google Scholar 

  • Van Raan, A. F. J., Handbook of Quantitative Studies of Science and Technology, Amsterdam: North-Holland, 1988.

    Google Scholar 

  • Van Raan, A. F. J., Advanced Bibliometric Methods to Assess Research Performance and Scientific Development: Basic Principles and Recent Practical Applications. University of Leiden report, CWTS- 93-05, 1993.

  • Ringle, C., Wende, S., Will, A., SmartPLS. University of Hamburg, Hamburg (http://www.smartpls.de), 2005.

    Google Scholar 

  • Romer, P. M. (1990), Endogenous technological change, Journal of Political Economy, 98(5): 71–102.

    Article  Google Scholar 

  • Sohn, S. Y., Joo, Y. G., Han, H. K. (2007), Structural equation model for the evaluation of national funding on R&D project of SMEs in consideration with MBNQA criteria, Evaluation and Program Planning, 30: 10–20.

    Article  Google Scholar 

  • Tenenhaus, M., Vinzia, V. E., Chatelin, Y. M., Lauro, C. (2005), PLS path modelling, Computational Statistics & Data Analysis, 48: 159–205.

    Article  MATH  MathSciNet  Google Scholar 

  • Virgo, J. A. (1977), A statistical procedure for evaluating the importance of scientific papers, The Library Quarterly, 47(4): 415–430.

    Article  Google Scholar 

  • Wold, H., Soft modelling: the basic design and some extensions, In: Jöreskog, K. G., Wold, H. (Eds), System under Indirect Observation. Amsterdam: North Holland, 1982.

    Google Scholar 

  • Wold, H., Partial least squares, In: Kotz, S., Johnson, N. L. (Eds), Encyclopaedia of Statistical Sciences, New York: Wiley, 1985.

    Google Scholar 

  • Yang, T. L., Chung, H. S. (1996), An international comparative study of basic scientific research capacity: OECD Countries, Taiwan and Korea, Technological Forecasting and Social Change, 52: 75–94.

    Article  Google Scholar 

  • Yoon, Y. S., Gursoy, D., Chen, J. S. (2001), Validating a tourism development theory with structural equation modelling, Tourism Management, 22: 363–372.

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Jiancheng Guan.

Additional information

The authors’ names are alphabetically ordered and they contributed equally to this paper.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Guan, J., Ma, N. Structural equation model with PLS path modeling for an integrated system of publicly funded basic research. Scientometrics 81, 683–698 (2009). https://doi.org/10.1007/s11192-009-2058-7

Download citation

  • Received:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s11192-009-2058-7

Keywords

Navigation