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Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs

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  • Won-Sik Hwang

    (Jeonbuk National University)

  • Ho-Sung Kim

    (Business Administration Department, Korea Army Academy At Yeong - Cheon (KAAY))

Abstract

Despite the proliferation of innovative technologies during the Fourth Industrial Revolution (4IR), there is a severe lack of quantitative and empirical studies that deal with the effectiveness of recently emerging technologies. This study examines the impact of employing core technologies of the 4IR on small and medium enterprises (SMEs). We used the firm-level cross-sectional data on Korean manufacturing SMEs, including the information on technology utilization. The stochastic production frontier estimation with selectivity correction is employed, besides matching technique to obtain unbiased estimates on technology efficiency. The empirical analysis finds that adopting emerging technologies enhances the productivity of SMEs. After observed and unobserved factors are controlled, the technical efficiency of adopters is higher by more than 26% on average, compared to non-adopters. Moreover, if the gap among production frontiers is considered, the difference in productivity would rise further. Additionally, a strategic alliance is a crucial route for SMEs to accept new technologies.

Suggested Citation

  • Won-Sik Hwang & Ho-Sung Kim, 2022. "Does the adoption of emerging technologies improve technical efficiency? Evidence from Korean manufacturing SMEs," Small Business Economics, Springer, vol. 59(2), pages 627-643, August.
    • Handle: RePEc:kap:sbusec:v:59:y:2022:i:2:d:10.1007_s11187-021-00554-w
    DOI: 10.1007/s11187-021-00554-w
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    1. Joel A. C. Baum & Tony Calabrese & Brian S. Silverman, 2000. "Don't go it alone: alliance network composition and startups' performance in Canadian biotechnology," Strategic Management Journal, Wiley Blackwell, vol. 21(3), pages 267-294, March.
    2. Nathalie Greenana & Jacques Mairesse, 2000. "Computers And Productivity In France: Some Evidence," Economics of Innovation and New Technology, Taylor & Francis Journals, vol. 9(3), pages 275-315.
    3. Sakellaris, Plutarchos, 2004. "Patterns of plant adjustment," Journal of Monetary Economics, Elsevier, vol. 51(2), pages 425-450, March.
    4. Haller, Stefanie A. & Lyons, Seán, 2015. "Broadband adoption and firm productivity: Evidence from Irish manufacturing firms," Telecommunications Policy, Elsevier, vol. 39(1), pages 1-13.
    5. González-Flores, Mario & Bravo-Ureta, Boris E. & Solís, Daniel & Winters, Paul, 2014. "The impact of high value markets on smallholder productivity in the Ecuadorean Sierra: A Stochastic Production Frontier approach correcting for selectivity bias," Food Policy, Elsevier, vol. 44(C), pages 237-247.
    6. L. Becchetti & David Bedoya & L. Paganetto, 2003. "ICT Investment, Productivity and Efficiency: Evidence at Firm Level Using a Stochastic Frontier Approach," Journal of Productivity Analysis, Springer, vol. 20(2), pages 143-167, September.
    7. Jondrow, James & Knox Lovell, C. A. & Materov, Ivan S. & Schmidt, Peter, 1982. "On the estimation of technical inefficiency in the stochastic frontier production function model," Journal of Econometrics, Elsevier, vol. 19(2-3), pages 233-238, August.
    8. Subal Kumbhakar & Efthymios Tsionas & Timo Sipiläinen, 2009. "Joint estimation of technology choice and technical efficiency: an application to organic and conventional dairy farming," Journal of Productivity Analysis, Springer, vol. 31(3), pages 151-161, June.
    9. Erik Brynjolfsson & Lorin M. Hitt, 2003. "Computing Productivity: Firm-Level Evidence," The Review of Economics and Statistics, MIT Press, vol. 85(4), pages 793-808, November.
    10. Torchia, Mariateresa & Calabrò, Andrea & Gabaldon, Patricia & Kanadli, Sadi Bogac, 2018. "Women directors contribution to organizational innovation: A behavioral approach," Scandinavian Journal of Management, Elsevier, vol. 34(2), pages 215-224.
    11. Ma, Wanglin & Renwick, Alan & Yuan, Peng & Ratna, Nazmun, 2018. "Agricultural cooperative membership and technical efficiency of apple farmers in China: An analysis accounting for selectivity bias," Food Policy, Elsevier, vol. 81(C), pages 122-132.
    12. Meeusen, Wim & van den Broeck, Julien, 1977. "Efficiency Estimation from Cobb-Douglas Production Functions with Composed Error," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 18(2), pages 435-444, June.
    13. Concetta Castiglione, 2012. "Technical efficiency and ICT investment in Italian manufacturing firms," Applied Economics, Taylor & Francis Journals, vol. 44(14), pages 1749-1763, May.
    14. Chen, Jie & Leung, Woon Sau & Evans, Kevin P., 2018. "Female board representation, corporate innovation and firm performance," Journal of Empirical Finance, Elsevier, vol. 48(C), pages 236-254.
    15. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    16. Ayed Mouelhi, Rim Ben, 2009. "Impact of the adoption of information and communication technologies on firm efficiency in the Tunisian manufacturing sector," Economic Modelling, Elsevier, vol. 26(5), pages 961-967, September.
    17. Huggett, Mark & Ospina, Sandra, 2001. "Does productivity growth fall after the adoption of new technology?," Journal of Monetary Economics, Elsevier, vol. 48(1), pages 173-195, August.
    18. Boris Bravo-Ureta & William Greene & Daniel Solís, 2012. "Technical efficiency analysis correcting for biases from observed and unobserved variables: an application to a natural resource management project," Empirical Economics, Springer, vol. 43(1), pages 55-72, August.
    19. Yam, Richard C. M. & Guan, Jian Cheng & Pun, Kit Fai & Tang, Esther P. Y., 2004. "An audit of technological innovation capabilities in chinese firms: some empirical findings in Beijing, China," Research Policy, Elsevier, vol. 33(8), pages 1123-1140, October.
    20. Tsuyoshi Nakamura & Hiroshi Ohashi, 2008. "Effects Of Technology Adoption On Productivity And Industry Growth: A Study Of Steel Refining Furnaces," Journal of Industrial Economics, Wiley Blackwell, vol. 56(3), pages 470-499, September.
    21. Heckman, James, 2013. "Sample selection bias as a specification error," Applied Econometrics, Russian Presidential Academy of National Economy and Public Administration (RANEPA), vol. 31(3), pages 129-137.
    22. Hung-pin Lai, 2015. "Maximum likelihood estimation of the stochastic frontier model with endogenous switching or sample selection," Journal of Productivity Analysis, Springer, vol. 43(1), pages 105-117, February.
    23. George E. Battese & D. S. Prasada Rao, 2002. "Technology Gap, Efficiency, and a Stochastic Metafrontier Function," International Journal of Business and Economics, School of Management Development, Feng Chia University, Taichung, Taiwan, vol. 1(2), pages 87-93, August.
    24. B. K. Atrostic & Sang V. Nguyen, 2005. "It and Productivity in U.S. Manufacturing: Do Computer Networks Matter?," Economic Inquiry, Western Economic Association International, vol. 43(3), pages 493-506, July.
    25. Kodde, David A & Palm, Franz C, 1986. "Wald Criteria for Jointly Testing Equality and Inequality Restriction s," Econometrica, Econometric Society, vol. 54(5), pages 1243-1248, September.
    26. Nicola Matteucci & Mary O'Mahony & Catherine Robinson & Thomas Zwick, 2005. "Productivity, Workplace Performance And Ict: Industry And Firm‐Level Evidence For Europe And The Us," Scottish Journal of Political Economy, Scottish Economic Society, vol. 52(3), pages 359-386, July.
    27. Robert Seamans & Manav Raj, 2018. "AI, Labor, Productivity and the Need for Firm-Level Data," NBER Working Papers 24239, National Bureau of Economic Research, Inc.
    28. repec:zwi:journl:v:43:y:2012:i:1:p:55-72 is not listed on IDEAS
    29. Aigner, Dennis & Lovell, C. A. Knox & Schmidt, Peter, 1977. "Formulation and estimation of stochastic frontier production function models," Journal of Econometrics, Elsevier, vol. 6(1), pages 21-37, July.
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    Cited by:

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    2. Ye, Fei & Ouyang, You & Li, Yina, 2023. "Digital investment and environmental performance: The mediating roles of production efficiency and green innovation," International Journal of Production Economics, Elsevier, vol. 259(C).
    3. Marlini Moodley & Dipolelo Fungile & Farai Nyika & Winiswa Mavutha, 2023. "Understanding Marketing Communications Strategies During and Post Covid 19: A South African Perspective," International Review of Management and Marketing, Econjournals, vol. 13(2), pages 36-46, March.
    4. Bettiol, Marco & Capestro, Mauro & Di Maria, Eleonora & Ganau, Roberto, 2024. "Is this time different?: how Industry 4.0 affects firms' labor productivity," LSE Research Online Documents on Economics 124545, London School of Economics and Political Science, LSE Library.
    5. Mauro Caselli & Edwin Fourrier-Nicolai & Andrea Fracasso & Sergio Scicchitano, 2024. "Digital Technologies and Firms’ Employment and Training," CESifo Working Paper Series 11056, CESifo.

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    More about this item

    Keywords

    Fourth Industrial Revolution; Stochastic production frontier; Propensity score matching; Sample selection; Technical efficiency; C21; D24; O33;
    All these keywords.

    JEL classification:

    • C21 - Mathematical and Quantitative Methods - - Single Equation Models; Single Variables - - - Cross-Sectional Models; Spatial Models; Treatment Effect Models
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O33 - Economic Development, Innovation, Technological Change, and Growth - - Innovation; Research and Development; Technological Change; Intellectual Property Rights - - - Technological Change: Choices and Consequences; Diffusion Processes

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