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Competitiveness of nations: a knowledge discovery examination. (English) Zbl 1155.62499

Summary: This paper presents the insights gained from the use of data mining and multivariate statistical techniques to identify important factors associated with a country’s competitiveness and the development of knowledge discovery in database (KDD) models to predict it. In addition to stepwise regression and weighted nonlinear programming techniques, intelligent learning techniques (artificial neural networks), and inferential techniques (classification and regression trees), were applied to a dataset of 43 countries from the World Competitiveness Yearbook (WCY). The dataset included 55 variables on economic internationalization, governmental, financial, infrastructure, management science and technology, as well as demographic and cultural characteristics. Exploratory data analysis and parameter calibration of the intelligent method architectures preceded the development and evaluation of reasonably accurate models (mean absolute error \(<5.5\%\)), and subsequent out-of-sample validations. The strengths and weaknesses of each of the KDD techniques were assessed, along with their relative performance and the primary input variables influencing a country’s competitiveness. Our analysis reveals that the primary drivers of competitiveness are lower country risk rating and higher computer usage, in entrepreneurial urbanized societies with less male dominance and basic infrastructure, with higher gross domestic investment, savings and private consumption, more imports of goods and services than exports, increased purchase power parity GDP, larger and more productive but not less expensive labor force, and higher R&D expenditures. Without diminishing the role and importance of WCY reports, our approach can be useful to estimate the competitiveness of many countries not included in WCY, while our findings may benefit policy makers and international agencies to expand their own abilities, insights and establish priorities for improving country competitiveness.

MSC:

62P99 Applications of statistics
62P20 Applications of statistics to economics
62P25 Applications of statistics to social sciences
62M45 Neural nets and related approaches to inference from stochastic processes
68P99 Theory of data
68P10 Searching and sorting

Software:

Excel; bootstrap
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Full Text: DOI

References:

[1] Apte, C.; Hong, S., Predicting equity returns from securities data, (Fayyad, U.; Piatetsky-Shapiro, G.; Smythand, P.; Uthurusamy, R., Advances in Knowledge Discovery and Data Mining (1996), AAAI Press: AAAI Press Menlo Park, CA), and MIT Press, Cambridge, MA, pp. 542-560
[2] Apte, C.; Weiss, S., Data mining with decision trees and decision rules, Future Generation Computer Systems, 13, 197-210 (1997)
[3] Artto, E. W., Relative total costs-An approach to competitiveness measurement of industries, Management International Review, 27, 47-58 (1987)
[4] Barnard, E.; Wessels, L., Extrapolation and interpolation in neural network classifiers, IEEE Control Systems, 12, 5, 50-53 (1992)
[5] Becerra-Fernandez, I., Searching for experts with expertise-locator knowledge management systems at NASA, Knowledge Management Review, 4, 4, 34-37 (2001)
[6] Bellman, R., Adaptive control processes: A guided tour (1961), Rand Corp: Rand Corp Santa Monica, CA · Zbl 0103.12901
[7] Benjamin, C. O.; Chi, S.; Gaber, T.; Riordan, C. A., Comparing BP and ART II neural network classifiers for facility location, Computers and Industrial Engineering, 28, 1, 43-50 (1995)
[8] Berry, M., Linoff, G., 1997. Data Mining Techniques for Marketing, Sales and Customer Support, John Wiley & Sons, New York; Berry, M., Linoff, G., 1997. Data Mining Techniques for Marketing, Sales and Customer Support, John Wiley & Sons, New York
[9] Bishop, C., Neural networks and their applications, Review of Scientific Instruments, 65, 6, 1803-1832 (1994)
[10] Brieman, L., Heuristics of instability and stabilization in model selection, Annals of Statistics, 24, 2350-2383 (1996) · Zbl 0867.62055
[11] Breiman, L.; Friedman, J.; Olshen, R.; Stone, C., Classification and Regression Trees (1984), Chapman & Hall: Chapman & Hall Boca Raton, FL · Zbl 0541.62042
[12] Cho, D. S.; Moon, H. C., A nation’s international competitiveness in different stages of economic development, Advances in Competitiveness Research, 1, 6, 5-19 (1998)
[13] Clark, J.; Guy, K., Innovation and competitiveness: A review, Technology Analysis and Strategic Management, 10, 3, 363-395 (1998)
[14] Dempster, A. P.; Laird, N. M.; Rubin, D. B., Maximum likelihood from incomplete data via the EM algorithm, Journal of the Royal Statistical Society, 39, 1, 1-38 (1977) · Zbl 0364.62022
[15] Drucker, P., The age of social transformation, The Atlantic Monthly, November, 274, 5, 53-71 (1994)
[16] Efron, B., The Jackknife, the Bootstrap and Other Resampling Plans (1982), Siam: Siam Philadelphia, PA · Zbl 0496.62036
[17] Efron, B.; Tibshirani, R. J., An Introduction to the Bootstrap (1993), Chapman & Hall: Chapman & Hall New York · Zbl 0835.62038
[18] Enoch, C. A., Measures of international trade, Bank of England Quarterly Bulletin, 18, 2 (1978)
[19] Fayyad, U.; Djorgovski, G.; Weir, N., Automating the analysis and cataloging of sky surveys, (Fayyad, U.; Piatetsky-Shapiro, G.; Smythand, P.; Uthurusamy, R., Advances in Knowledge Discovery and Data Mining (1996), AAAI Press: AAAI Press Menlo Park, CA), 471-4933, and MIT Press, Cambridge, MA
[20] Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R., From data mining to knowledge discovery: An overview, (Fayyad, U.; Piatetsky-Shapiro, G.; Smyth, P.; Uthurusamy, R., Advances in Knowledge Discovery and Data Mining (1996), AAAI Press: AAAI Press Menlo Park, CA/MIT Press, Cambridge), 1-33
[21] Fu, L., Neural Networks in Computer Intelligence (1994), McGraw-Hill: McGraw-Hill New York
[22] Fylstra, D.; Lasdon, L.; Watson, J.; Waren, A., Design and use of the Microsoft Excel solver, Interfaces, 28, 5, 29-55 (1998)
[23] Garelli, S., Competitiveness of Nations: The Fundamentals (2003), IMD World Competitiveness Yearbook: IMD World Competitiveness Yearbook Lausanne, Switzerland
[24] Hair, J. F.; Anderson, R. E.; Tatham, R. L.; Black, W. C., Multivariate Data Analysis (1998), Prentice Hall: Prentice Hall New Jersey
[25] Hartigan, J. A., Clustering Algorithms (1975), John Wiley & Sons, Inc: John Wiley & Sons, Inc New York · Zbl 0321.62069
[26] Hofstede, G., Cultures and Organizations (1991), McGraw Hill: McGraw Hill London
[27] Hornik, K.; Stinchcombe, M.; White, H., Multilayer feedforward networks are universal approximators, Neural Networks, 2, 5, 359-366 (1989) · Zbl 1383.92015
[28] Ivanova, I. M.; Arcelus, F. J.; Srinivasan, G., Assessment of the competitiveness position of the Latin American countries, International Journal of Commerce and Management, 8, 2, 7-32 (1998)
[29] Kao, C.; Liu, S. T., Competitiveness of manufacturing firms: An application of fuzzy weighted average, IEEE Transactions on Systems, Man and Cybernetics. Part A: Systems and Humans, 29, 6, 661-667 (1999)
[30] Karnani, A., Equilibrium market share-A measure of competitive strength, Strategic Management Journal, 3, 43-51 (1982)
[31] Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. Available from <http://robotics.stanford.edu/ ronnyk/; Kohavi, R., 1995. A study of cross-validation and bootstrap for accuracy estimation and model selection. International Joint Conference on Artificial Intelligence. Available from <http://robotics.stanford.edu/ ronnyk/
[32] Kovalerchuck, B.; Triantaphyllou, E.; Ruiz, J.; Torvik, V.; Vityaev, E., The reliability issue of computer-aided breast cancer diagnosis, Journal of Computers and Biomedical Research, 33, 4, 296-313 (2000)
[33] Li, Y.; Deng, S., A methodology for competitive advantage analysis and strategy formulation: An example in a transitional economy, European Journal of Operational Research, 118, 2, 259-270 (1999) · Zbl 0939.91074
[34] Lim, T.; Loh, W.; Yu-Shan, S., A comparison of prediction accuracy, complexityand training time of thirty-three old and new classification algorithms, Machine Learning, 40, 203-229 (2000) · Zbl 0969.68669
[35] Little, J. A., Regression with missing X’s: A review, Journal of the American Statistical Association, 87, 1227-1237 (1992)
[36] Menzler-Hokkanen, I., Can international competitiveness be measured by the relative unit labour cost approach? A comment on professor Artto, Management International Review, 29, 1, 72-77 (1989)
[37] Nasierowski, W.; Arcelus, F. J., Interrelationships among the elements of national innovation systems: A statistical evaluation, European Journal of Operational Research, 235-253 (1999)
[38] Nasierowski, W.; Arcelus, F. J., On the stability of countries’ national technological systems, (Zanakis, S. H.; Doukidis, G.; Zopounidis, C., Decision Making: Recent Developments and Worldwide Applications (2000), Kluwer Academic Publishers: Kluwer Academic Publishers Boston), 97-111
[39] Oral, M., Forecasting industrial competitiveness, International Journal of Forecasting, 1, 1, 49-62 (1985)
[40] Oral, M., A methodology for competitiveness analysis and strategy formulation in glass industry, European Journal of Operational Research, 68, 9-22 (1993)
[41] Oral, M.; Chabchoub, H., On the methodology of the world competitiveness report, European Journal of Operational Research, 90, 3, 514-535 (1996) · Zbl 0907.90105
[42] Oral, M.; Cinar, U.; Chabchoub, H., Linking industrial competitiveness and productivity at the firm level, European Journal of Operational Research, 118, 2, 271-277 (1999) · Zbl 0939.91075
[43] Oral, M.; Ozkan, O. A., An empirical study on measuring industrial competitiveness, Journal of Operational Research Society, 37, 4, 345-356 (1986)
[44] Peterson, J.; Barras, R., Measuring international competitiveness in services, Services Industry Journal, 7, 2, 131 (1987)
[45] Porter, M. E., The Competitive Advantage of Nations (1990), The Free Press: The Free Press MacMillan, New York
[46] Rugman, A. M.D.; Cruz, J. R., The “double diamond” model of international competitiveness: The Canadian experience, Management International Review, 33, 2, 17-32 (1993)
[47] Shao, J., An asymptotic theory for linear model selection, Statistica Sinica, 7, 221-264 (1997) · Zbl 1003.62527
[48] Smith, K. A.; Gupta, J. N.D., Neural networks in business: Techniques and applications for the operations researcher, Computers and Operations Research, 27, 1023-1044 (2000) · Zbl 0961.90016
[49] Schürmann, J., Pattern Classification: A Unified View of Statistical and Neural Approaches (1996), Wiley: Wiley New York
[50] SPSS, 2000a. Data Mining: Modeling. Chicago, IL; SPSS, 2000a. Data Mining: Modeling. Chicago, IL
[51] SPSS, 2000b. Advanced Modeling with Clementine. Chicago, IL; SPSS, 2000b. Advanced Modeling with Clementine. Chicago, IL
[52] Stevens, L., 2001. IT sharpens data mining’s focus. Internet Week, August 6; Stevens, L., 2001. IT sharpens data mining’s focus. Internet Week, August 6
[53] Stone, M., Cross-validation choice and assessment of statistical predictions, Journal of the Royal Statistical Society B, 36, 111-147 (1974) · Zbl 0308.62063
[54] US President’s Commission on Industrial Competitiveness, 1985. Global competition: The new reality. US Government Printing Office, Washington, DC; US President’s Commission on Industrial Competitiveness, 1985. Global competition: The new reality. US Government Printing Office, Washington, DC
[55] Velocci, A. L., More rigorous methodology applied to gauge competitiveness, Aviation Week and Space Technology, 149, 6, 67-68 (1998)
[56] Walczak, S., Neural network models for a resource allocation problem, IEEE Transactions on Systems, Man and Cybernetics, 28, 2, 276-284 (1998)
[57] Walczak, S., An empirical analysis of data requirements for financial forecasting with neural networks, Journal of Management Information Systems, 17, 4, 203-222 (2001)
[58] Walczak, S., Neural networks as a tool for developing and validating business heuristics, Expert Systems with Applications, 21, 1, 31-36 (2001)
[59] Walczak, S.; Cerpa, N., Heuristic principles for the design of artificial neural networks, Information and Software Technology, 41, 2, 109-119 (1999)
[60] Wehrens, R.; Putter, H.; Buydens, L. M.C., The boostrap: A tutorial, Chemometrics and Intelligent Laboratory Systems, 54, 35-52 (2000)
[61] Widrow, B.; Rumelhart, D. E.; Lehr, M. A., Neural networks: Applications in industry, business and science, Communications of the ACM, 37, 3, 93-105 (1994)
[62] Wong, B. K.; Bodnovich, T. A.; Selvi, Y., Neural network applications in business: A review and analysis of the literature (1988-1995), Decision Support Systems, 19, 301-320 (1997)
[63] World Competitiveness Yearbook, 1999. Institute for Management Development, Lausanne, Switzerland; World Competitiveness Yearbook, 1999. Institute for Management Development, Lausanne, Switzerland
[64] Yamin, S.; Mavondo, F.; Gunasekaran, A.; Sarros, J. C., A study of competitive strategy, organisational innovation and organisational performance among Australian manufacturing companies, International Journal of Production Economics, 52, 1-2, 161-172 (1997)
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