Politická ekonomie 2022, 70(3):312-340 | DOI: 10.18267/j.polek.1352

Estimates of Future Industrial Development in the Context of Company Size

Lucie Povolná ORCID...a, Michaela Jánská ORCID...a, Marta ®ambochová ORCID...b
a Department of Economics and Management, Faculty of Social and Economic Studies, Jan Evangelista Purkyně University in Ústí nad Labem, Czech Republic
b Department of Mathematics and Statistics, Faculty of Social and Economic Studies, Jan Evangelista Purkyně University in Ústí nad Labem, Czech Republic


The expected development of economic reality is a determining variable for many companies and their demand planning. Do their reflections on future market development differ depending on how big the companies are? The study focused on business cycle indicators that the Czech Statistical Office (CZSO) publishes regularly. Until 2017, the CZSO published these indicators sorted according to company size, but then it abandoned the division. This study aims to evaluate whether the size of companies affects their estimate of future demand and to use these results to point out whether the termination of the publishing of these indicators, broken down by company size, was justified. The data were evaluated with correlation and cluster analysis. The research confirms that the nature of the forecasts for different-sized companies varies in terms of examined prediction indicators. Small and medium-sized companies agree in their projections, and large companies (in general) are more pessimistic than small and medium-sized ones. The breakdown made according to the size of companies should be maintained as it is an essential signal for policymakers.

JEL classification: E66, M21, O50

Received: June 29, 2021; Revised: January 26, 2022; Accepted: February 14, 2022; Published: July 4, 2022  Show citation

ACS AIP APA ASA Harvard Chicago IEEE ISO690 MLA NLM Turabian Vancouver
Povolná, L., Jánská, M., & ®ambochová, M. (2022). Estimates of Future Industrial Development in the Context of Company Size. Politická ekonomie, 70(3), 312-340. doi: 10.18267/j.polek.1352
Download citation
  • BibTeX (.bib)
  • Bookends (.ris)
  • EasyBib (.ris)
  • EndNote (.enw)
  • EndNote 8 (.xml)
  • ISI WoS (.isi)
  • Medlars (.medlars)
  • Mendeley (.ris)
  • MODS (.xml)
  • Papers (.ris)
  • RefWorks (.txt)
  • RefManager (.ris)
  • RIS (.ris)
  • MS Word (.xml)
  • Zotero (.ris)
    • Open full article

    References

    1. Adam, N. A., Alarifi, G. (2021). Innovation practices for survival of small and medium enterprises (SMEs) in the COVID-19 times: the role of external support. Journal of Innovation and Entrepreneurship, 10(1), https://doi.org/10.1186/s13731-021-00156-6 Go to original source...
    2. Alecke, B., Mitze, T., Reinkowski, J., et al. (2012). Does Firm Size Make a Difference? Analysing the Effectiveness of R&D Subsidies in East Germany. German Economic Review, 13(2), 174-195, https://doi.org/10.1111/j.1468-0475.2011.00546.x Go to original source...
    3. Anderson, Ch. K., Carroll, B. (2007). Demand management: Beyond revenue management. Journal of Revenue and Pricing Management, 6(4), 240-263, https://doi.org/10.1057/palgrave.rpm.5160092 Go to original source...
    4. Archibugi, D. (2015). Blade Runner Economics: Will Innovation Lead the Economic Recovery? Forthcoming in Research Policy, 46(3), https://doi.org/10.2139/ssrn.2557335 Go to original source...
    5. Ariño, A., Ragozzino, R., Reuer, J. J. (2008). Alliance dynamics for entrepreneurial firms. Journal of Management Studies, 45(1), 147-168, https://doi.org/10.1111/j.1467-6486.2007.00745.x Go to original source...
    6. Arranz, N., Arroyabe, M. F., Li, J., et al. (2019). An Integrated Model of Organisational Innovation and Firm Performance: Generation, Persistence and Complementarity. Journal of Business Research, 105(12), 270-282, https://doi.org/10.1016/j.jbusres.2019.08.018 Go to original source...
    7. Baardman, L., Levin, I., Perakis, G., et al. (2018). Leveraging Comparables for new product sales forecasting. Production and Operations Management, 27(12), 2340-2343, https://doi.org/10.2139/ssrn.3086237 Go to original source...
    8. Bachmann, R., Elstner, S., Sims, E. R. (2013). Uncertainty and Economic Activity: Evidence from Business Survey Data. American Economic Journal: Macroeconomics, 5(2), 217-249, https://doi.org/10.1257/mac.5.2.217 Go to original source...
    9. Biermauer-Polly, J., Hölz, W. (2016). Business Cycle Dynamics and Firm Heterogeneity: Evidence for Austria Using Survey Data. WIFO Working Paper No. 504. Available at: https://www.econstor.eu/bitstream/10419/129053/1/wp_504.pdf
    10. Biggs, T. (2003). Is Small Beautiful and Worthy of Subsidy? Literature Review. Washington, D.C.: International Finance Corporation (IFC). Available at: http://citeseerx.ist.psu.edu/viewdoc/download?doi=10.1.1.113.385&rep=rep1&type=pdf
    11. Boone, T., Ganeshan, R., Jain, A., et al. (2019). Forecasting sales in the supply chain: consumer analytics in the big data era. International Journal of Forecasting, 35(1), 170-180, https://doi.org/10.1016/j.ijforecast.2018.09.003 Go to original source...
    12. Belt, P., Harkonen, J., Mottonen, M., et al. (2010). Technological uncertainty and verification and validation activities. International Journal of Innovation and Learning, 7(2), 223-243, https://doi.org/10.1504/ijil.2010.030615 Go to original source...
    13. Brown, E. (2011). Japan earthquake shifted Earth on its axis. Los Angeles Times [online]. Available at: https://www.latimes.com/science/la-sci-japan-quake-science-20110313-story.html
    14. Calof, J. L., Wright, S., Trim, P. R., et al. (2008a). A strategic marketing intelligence and multi-organisational resilience framework. European Journal of Marketing, 42(7/8), 731-745, https://doi.org/10.1108/03090560810877123 Go to original source...
    15. Calof, J. L., Wright, S., Fleisher, C. S. (2008b). Using open source data in developing competitive and marketing intelligence. European Journal of Marketing, 42(7/8), 852-866, https://doi.org/10.1108/03090560810877196 Go to original source...
    16. Chen, A., Blue, J. (2010). Performance analysis of demand planning approaches for aggregating, forecasting and disaggregating interrelated demands. International Journal of Production Economics, 128(2), 586-602, https://doi.org/10.1016/j.ijpe.2010.07.006 Go to original source...
    17. Cowling, M., Liu, W., Zhang, N. (2018). Did firm age, experience, and access to finance count? SME performance after the global financial crisis. Journal of Evolutionary Economics, 28(1), 77-100, https://doi.org/10.1007/s00191-017-0502-z Go to original source...
    18. Criscuolo, C., Martin, R., Overman, H. G., et al. (2019). Some Causal Effects of an Industrial Policy. American Economic Review, 109(1), 48-85, https://doi.org/10.1257/aer.20160034 Go to original source...
    19. Croxton, K. L., Lambert, D. M., García-Dastugue, S. J., et al. (2002). The demand management process. The International Journal of Logistics Management, 13(2), 51-66, https://doi.org/10.1108/09574090210806423 Go to original source...
    20. CZSO (2015). Business Cycle Surveys - Methodology. Prague: Czech Statistical Office. Available at: https://www.czso.cz/csu/czso/business_cycle_surveys
    21. CZSO (2017). Konjunkturální průzkum v podnicích průmyslových, stavebních, obchodních a ve vybraných odvětvích sluľeb - prosinec 2017. Prague: Czech Statistical Office. Available at: https://www.czso.cz/csu/czso/konjunkturalni-pruzkum-v-podnicich-prumyslovych-stavebnich-obchodnich-a-ve-vybranych-odvetvich-sluzeb-prosinec-2017
    22. De Mattos, C., Burgess, T. F., Shaw, N. E. (2013). The impact of R&D-specific factors on the attractiveness of small- and medium-sized enterprises as partners vis-a-vis alliance formation in large emerging economies. R&D Management, 43(1), 1-20, https://doi.org/10.1111/j.1467-9310.2012.00699.x Go to original source...
    23. Dickson, P. H., Weaver, K. M., Hoy, F. (2006). Opportunism in the R&D alliances of SMEs: the roles of the institutional environment and SME size. Journal of Business Venturing, 21(4), 487-513, https://doi.org/10.1016/j.jbusvent.2005.02.003 Go to original source...
    24. Dollinger, M. J., Kolchin, M. G. (1985). Purchasing and the Small Firm. American Journal of Small Business, 10(3), 33-45, https://doi.org/10.1177/104225878501000304 Go to original source...
    25. Dovern, J., Jannsen, N. (2017). Systematic errors in growth expectations over the business cycle. International Journal of Forecasting, 33(4), 760-769, https://doi.org/10.1016/j.ijforecast.2017.03.003 Go to original source...
    26. Drehmann, M., Juselius, M. (2014). Evaluating early warning indicators of banking crises: Satisfying policy requirements. International Journal of Forecasting, 30(3), 759-780, https://doi.org/10.1016/j.ijforecast.2013.10.002 Go to original source...
    27. Dvouletý, O. (2019). Development of Entrepreneurial Activity in the Czech Republic over the Years 2005-2017. Journal of Open Innovation: Technology, Market, and Complexity, 5(3), https://doi.org/10.3390/joitmc5030038 Go to original source...
    28. Dvouletý, O., Blaľková I., Potluka, O. (2021a). Estimating the effects of public subsidies on the performance of supported enterprises across firm sizes, Research Evaluation, 30(3), 290-313, https://doi.org/10.1093/reseval/rvab004 Go to original source...
    29. Dvouletý, O., Srhoj, S., Pantea, S. (2021b). Public SME Grants and Firm Performance in European Union: A Systematic Review of Empirical Evidence. Small Business Economics, 57, 243-263, https://doi.org/10.1007/s11187-019-00306-x Go to original source...
    30. Espinoza, M., Joye, C., Belmans, R., et al. (2005). Short-term load forecasting, profile identification, and customer segmentation: a methodology based on periodic time series. IEEE Transactions on Power Systems, 20(3), https://doi.org/10.1109/tpwrs.2005.852123 Go to original source...
    31. Ettorre, B. (1995). Managing competitive intelligence. Management review, 84(10), 15-19.
    32. EC (2016). The Joint Harmonised EU Programme of Business and Consumer Surveys. Brussels: European Commission. Available at: https://ec.europa.eu/economy_finance/db_indicators/surveys/documents/bcs_user_guide_en.pdf
    33. EC (2017). The Joint Harmonised EU Programme of Business and Consumer Surveys, User Guide. Brussels: European Commission. Available at: https://ec.europa.eu/info/sites/default/files/bcs_user_guide.pdf
    34. EP (2017). Odpověď EU na migraci. Available at: https://www.europarl.europa.eu/news/cs/headlines/society/20170629STO78629/odpoved-eu-na-migraci
    35. Fabrizio, K. R., Tsolmon, U. (2014). An empirical examination of the procyclicality of R&D investment and innovation. The Review of Economics and Statistics, 96(4), 662-675, https://doi.org/10.1162/rest_a_00412 Go to original source...
    36. Fildes, R., Goodwin, P., Lawrence, M., et al. (2009). Effective forecasting and judgmental adjustments: an empirical evaluation and strategies for improvement in supply-chain planning. International Journal of Forecasting, 25(1), 3-23, https://doi.org/10.1016/j.ijforecast.2008.11.010 Go to original source...
    37. Fuld, L. M. (1995). The New Competitor Intelligence. Competitive Intelligence Review, 6(3), 85-86, https://doi.org/10.1002/cir.3880060323 Go to original source...
    38. Gadde, L. E., Håkansson, H. (2001). Supply network strategies. Australia: John Wiley & Sons. ISBN 978-0471499169.
    39. García-Ferrer, A., Bujosa, M. B. (2000). Forecasting OECD industrial turning points using unobserved components models with business survey data. International Journal of Forecasting, 16(2), 207-227, https://doi.org/10.1016/S0169-2070(99)00049-7 Go to original source...
    40. Hansson, J., Jansson, P., Löf, M. (2005). Business survey data: Do they help in forecasting GDP growth? International Journal of Forecasting, 21(2), 377-389, https://doi.org/10.1016/j.ijforecast.2004.11.003 Go to original source...
    41. Hariharan, V., Thangavel, N. (2016). Business Analytics - Enabling Toll for Micro, Small & Medium Enterprises. I J A B E R, 14(4), 2379-2387.
    42. Huster, M. (2005). Marketing Intelligence: A First Mover Advantage. Competitive Intelligence Magazine, 8(2), 13-17.
    43. Hyndman, R. J., Athanasopoulos, G. (2018). Forecasting: principles and practice. 4th ed. Melbourne: OTexts. ISBN 0987507117.
    44. Jain, A. K. (2010). Data clustering: 50 years beyond K-means. Pattern Recognition Letters, 31(8), 651-666, https://doi.org/10.1016/j.patrec.2009.09.011 Go to original source...
    45. Juergensen, J., Guimón, J., Narula, R. (2020). European SMEs amidst the COVID-19 crisis: assessing impact and policy responses. Journal of Industrial and Business Economics, 47, 499-510, https://doi.org/10.1007/s40812-020-00169-4 Go to original source...
    46. Jüttner, U., Christopher, M., Baker, S. (2007). Demand chain management-integrating marketing and supply chain management. Industrial Marketing Management, 36(3), 377-392, https://doi.org/10.1016/j.indmarman.2005.10.003 Go to original source...
    47. Kerkkänen, A., Korpela, J., Huiskonen, J. (2009). Demand forecasting errors in industrial context: measurement and impacts. International Journal of Production Economics, 118(1), 43-48, https://doi.org/10.1016/j.ijpe.2008.08.008 Go to original source...
    48. Kersten, R., Harms, J., Liket, K., et al. (2017). Small Firms, Large Impact? A Systematic Review of the SME Finance Literature. World Development, 97(9), 330-348, https://doi.org/10.1016/j.worlddev.2017.04.012 Go to original source...
    49. Kilger, Ch., Wagner, M. (2008). Demand planning. In: Stadtler, H., Kilger, Ch. (2008). Supply chain management and advanced planning. 4th ed. Berlin: Springer-Verlag. pp. 133-160. ISBN: 978-3-540-74512-9. Go to original source...
    50. Lackman, C., Saban, K., Lanasa, J. (2000). The contribution of market intelligence to tactical and strategic business decisions. Marketing Intelligence & Planning, 18(1), 6-9, https://doi.org/10.1108/02634500010308530 Go to original source...
    51. Lawrence, M., O'Connor, M., Edmundson, B. (2000). A field study of sales forecasting accuracy and processes. European Journal of Operational Research, 122(1), 151-160, https://doi.org/10.1016/s0377-2217(99)00085-5 Go to original source...
    52. Le, H. B. H., Nguyen, T. L., Ngo, C. T., et al. (2020). Policy related factors affecting the survival and development of SMEs in the context of Covid 19 pandemic. Management Science Letters, 10(15), 3683-3692, https://doi.org/10.5267/j.msl.2020.6.025 Go to original source...
    53. Lin, Y.-Ch., Huang, T.-H. (2012). Creative destruction over the business cycle: a stochastic frontier analysis. Journal of Productivity Analysis, 38(3), 285-302, https://doi.org/10.1007/s11123-012-0273-3 Go to original source...
    54. Lin, Y., Chen, Ch., Chao, Ch., et al. (2020). The Study of Evaluation Index of Growth Evaluation of Science and Technological Innovation Micro-Enterprises. Sustainability, 12(15), 6233, https://doi.org/10.3390/su12156233 Go to original source...
    55. Lohrke, F. T., Kreiser, P. M., Weaver, K. M. (2006). The influence of current firm performance on future SME alliance formation intentions: a six-country study. Journal of Business Research, 59(1), 19-27, https://doi.org/10.1016/j.jbusres.2005.02.002 Go to original source...
    56. Lojka, J., Obst, J., Zeman, J. (2016). Assessment of the Ability of the Business and Consumer Surveys to Predict the Gross Value Added of the Czech Republic. Statistika. 96(4), 19-36.
    57. Lu, C.-J., Kao, L.-J. (2016). A clustering-based sales forecasting scheme by using extreme learning machine and ensembling linkage methods with applications to computer server. Engineering Applications of Artificial Intelligence, 55(10), 231-238, https://doi.org/10.1016/j.engappai.2016.06.015 Go to original source...
    58. Marek, L., Hronová, S., Hindls, R. (2019). Moľnosti odhadů krátkodobých makroekonomických agregátů na základě výsledků konjunkturálních průzkumů. Politická ekonomie, 67(4),347-370, https://doi.org/10.18267/j.polek.1243 Go to original source...
    59. Marien, E. J. (1999). Demand planning and sales forecasting: A supply chain essential. Supply Chain Management Review, 2(4).
    60. Mateev, M., Anastasov, Y. (2010). Determinants of small and medium sized fast growing enterprises in Central and Eastern Europe: A panel data analysis. Financial Theory and Practice, 34(3), 269-295.
    61. Melnyk, S. A., Closs, D. J., Friffis, S. E., et al. (2014). Understanding supply chain resilience. Supply Chain Management Review, 18(1).
    62. Mentzer, J. T., Moon, M. A., Estampe, D., et al. (2007). Demand management. In: Mentzer, J. T., Myers, M. B., Stank, T. P. (2007). Handbook of Global Supply Chain Management. ISBN 9781412918053. Go to original source...
    63. Min, H., Yu, W. (2008). Collaborative planning, forecasting and replenishment: demand planning in supply chain management. International Journal of Information Technology and Management, 7(1), 4-20, https://dl.acm.org/doi/10.5555/1360270.1360271 Go to original source...
    64. Moon, M. A., Mentzer, J. T., Thomas Jr., D. E. (2000). Customer demand planning at Lucent Technologies: a case study in continuous improvement through sales forecast auditing. Industrial Marketing Management, 29(1), 19-26, https://doi.org/10.1016/S0019-8501(99)00108-X Go to original source...
    65. Narula, R. (2004). R&D collaboration by SMEs: new opportunities and limitations in the face of globalisation. Technovation, 24(2), 153-161, https://doi.org/10.1016/s0166-4972(02)00045-7 Go to original source...
    66. Nollet, J., Rebolledo, C., Popel, V. (2012). Becoming a preferred customer one step at a time. Industrial Marketing Management, 41(8), 1186-1193, https://doi.org/10.1016/j.indmarman.2012.10.003 Go to original source...
    67. Nováková, J. (2019). Podniky podle velikosti. Ekonomicky.eu [online]. Available at: https://www.ekonomicky.eu/podniky-podle-velikosti/
    68. OECD (2009). The Impact of the Global Crisis on SME and Entrepreneurship Financing and Policy Responses. Paris: OECD. Available at: http://www.oecd.org/industry/smes/49316499.pdf
    69. Pearson, J. N., Ellram, L. M. (1995). Supplier selection and evaluation in small versus large electronics firms. Journal of Small Business Management, 33(4).
    70. Pettit, T. J., Croxton, K., Fiksel, J. (2019). The evolution of resilience in supply chain management: a retrospective on ensuring supply chain resilience. Journal of Business Logistic, 40(1), 56-65, https://doi.org/10.1111/jbl.12202 Go to original source...
    71. Polyviou, M., Croxton, K. L., Knemeyer, A. M. (2019). Resilience of medium-sized firms to supply chain disruptions: the role of internal social capital. International Journal of Operations & Production Management, 40(1), 68-91, https://doi.org/10.1108/IJOPM-09-2017-0530 Go to original source...
    72. Povolná, L. (2019). Innovation Strategy in Small and Medium Sized Enterprises (SMEs) in the Context of Growth and Recession Indicators. Journal of Open Innovation:Technology, Market, and Complexity, 5(2), 32, https://doi.org/10.3390/joitmc5020032 Go to original source...
    73. Ptáčková, V., ©těpánek, L., Hanzal, V. (2018). Are industrial companies in the Czech Republic able to predict the short-term future of the economy? Prague: Oeconomica Publishing House, Prague University of Economics and Business. International Scientific Conference AMSE Applications of Mathematics and Statistics in Economics 2018, pp. 39-48. ISBN 978-80-245-2277-7.
    74. Ptáčková, V., Novák, J. (2020). Survey on survey in the Czech industry sector. Prague: Prague University of Economics and Business. Conference Proceedings FMSCICON2020, pp. 7-17. ISBN 978-80-245-2344-6.
    75. Rahmana, N. A., Yaacob, Z., Radzi, R. M. (2016). An Overview of Technological Innovation on SME Survival: A Conceptual Paper. Procedia - Social and Behavioral Sciences, 224(6), 508-515, https://doi.org/10.1016/j.sbspro.2016.05.427 Go to original source...
    76. Razumovskaia, E., Yuzvovich, L., Kniazeva, E., et al. (2020). The Effectiveness of Russian Government Policy to Support SMEs in the COVID-19 Pandemic. Journal of Open Innovation: Technology, Market, and Complexity, 6(4), 160, https://doi.org/10.3390/joitmc6040160 Go to original source...
    77. Sagaert, Y. R., Aghezzaf, E., Kourentzes, N., Desmet, B. (2018). Tactical sales forecasting using a very large set of macroeconomic indicators. European Journal of Operational Research, 264(2), 558-569, https://doi.org/10.1016/j.ejor.2017.06.054 Go to original source...
    78. Schumacher, E. F. (2011). Small is Beautiful: A Study of Economics as if People Mattered. London, UK: Abacus Publishing. ISBN 9780349131320.
    79. Silvestri, D., Riccaboni, M., Della Malva, A. (2018). Sailing in all winds: Technological search over the business cycle. Research Policy, 47(10), 1933-1944, https://doi.org/10.1016/j.respol.2018.07.002 Go to original source...
    80. Srinivasan, S. (2004). Role of trust in e-business success. Information Management & Computer Security, 12(1), 66-72, https://doi.org/10.1108/09685220410518838 Go to original source...
    81. Stadtler, H., Kilger, Ch. (2008). Supply chain management and advanced planning. 4th ed. Berlin: Springer-Verlag. ISBN: 978-3-540-74512-9.
    82. Steensma, H. K., Marino, L., Weaver, K. M., et al. (2000). The influence of national culture on the formation of technology alliances by entrepreneurial firms. Academy of Management Journal, 43(5), https://doi.org/10.5465/1556421 Go to original source...
    83. Tavassoli, S. (2015). Innovation determinants over industry life cycle. Technological Forecasting and Social Change, 91(2), 18-32, https://doi.org/10.1016/j.techfore.2013.12.027 Go to original source...
    84. Van Gils, A., Zwart, P. S. (2009). Alliance formation motives in SMEs an explorative conjoint analysis study. International Small Business Journal, 27(1), 5-37, https://doi.org/10.1177/0266242608098345 Go to original source...
    85. Verstraete, G., Aghezzaf, E.-H., Desmet, B. (2020). A leading macroeconomic indicators' based framework to automatically generate tactical sales forecasts. Computers & Industrial Engineering, 139(1), https://doi.org/10.1016/j.cie.2019.106169 Go to original source...
    86. Vláda České republiky (2012). Daňové změny od roku 2013. Vlada.cz [online]. Available at:https://www.vlada.cz/cz/media-centrum/tema/danove-zmeny-od-roku-2013-95853/
    87. Vochozka M., et al. (2020). Metody komplexního hodnocení podniku. 2nd ed. Praha: Grada. ISBN 978-80-271-1701-7.
    88. Wu, X., Kumar, V., Ross Quinlan, J. (2008). Top 10 algorithms in data mining. Knowledge and Information Systems, 14(1), 1-37, https://doi.org/10.1007/s10115-007-0114-2 Go to original source...

    This is an open access article distributed under the terms of the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License (CC BY NC ND 4.0), which permits non-comercial use, distribution, and reproduction in any medium, provided the original publication is properly cited. No use, distribution or reproduction is permitted which does not comply with these terms.


    Return to the content