IDEAS home Printed from https://ideas.repec.org/a/kap/sbusec/v59y2022i2d10.1007_s11187-021-00554-w.html
   My bibliography  Save this article

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

Author

Listed:
  • 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
    as

    Download full text from publisher

    File URL: http://link.springer.com/10.1007/s11187-021-00554-w
    File Function: Abstract
    Download Restriction: Access to full text is restricted to subscribers.
    File URL: https://libkey.io/10.1007/s11187-021-00554-w?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    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. 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.
    4. 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.
    5. 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.
    6. 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.
    7. Concetta Castiglione, 2012. "Technical efficiency and ICT investment in Italian manufacturing firms," Applied Economics, Taylor & Francis Journals, vol. 44(14), pages 1749-1763, May.
    8. 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.
    9. 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.
    10. 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.
    11. 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.
    12. 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.
    13. 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.
    14. 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.
    15. 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.
    16. Sakellaris, Plutarchos, 2004. "Patterns of plant adjustment," Journal of Monetary Economics, Elsevier, vol. 51(2), pages 425-450, March.
    17. 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.
    18. 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.
    19. 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.
    20. 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.
    21. 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.
    22. 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.
    23. 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.
    24. William Greene, 2010. "A stochastic frontier model with correction for sample selection," Journal of Productivity Analysis, Springer, vol. 34(1), pages 15-24, August.
    25. 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.
    26. 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.
    27. 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.
    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.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. 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).
    2. 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.

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. K Hervé Dakpo & Laure Latruffe & Yann Desjeux & Philippe Jeanneaux, 2022. "Modeling heterogeneous technologies in the presence of sample selection: The case of dairy farms and the adoption of agri‐environmental schemes in France," Agricultural Economics, International Association of Agricultural Economists, vol. 53(3), pages 422-438, May.
    2. Boris E. Bravo‐Ureta & Mario González‐Flores & William Greene & Daniel Solís, 2021. "Technology and Technical Efficiency Change: Evidence from a Difference in Differences Selectivity Corrected Stochastic Production Frontier Model," American Journal of Agricultural Economics, John Wiley & Sons, vol. 103(1), pages 362-385, January.
    3. Owusu, Eric S. & Bravo-Ureta, Boris E., 2022. "Reap when you sow? The productivity impacts of early sowing in Malawi," Agricultural Systems, Elsevier, vol. 199(C).
    4. Carrer, Marcelo José & Filho, Hildo Meirelles de Souza & Vinholis, Marcela de Mello Brandão & Mozambani, Carlos Ivan, 2022. "Precision agriculture adoption and technical efficiency: An analysis of sugarcane farms in Brazil," Technological Forecasting and Social Change, Elsevier, vol. 177(C).
    5. Bravo-Ureta, Boris E. & Higgins, Daniel & Arslan, Aslihan, 2020. "Irrigation infrastructure and farm productivity in the Philippines: A stochastic Meta-Frontier analysis," World Development, Elsevier, vol. 135(C).
    6. Kamiche Zegarra, J. & Bravo-Ureta, B., 2018. "Are users of market information efficient? A stochastic production frontier model corrected by sample selection," 2018 Conference, July 28-August 2, 2018, Vancouver, British Columbia 275870, International Association of Agricultural Economists.
    7. Maria Vrachioli & Spiro E. Stefanou & Vangelis Tzouvelekas, 2021. "Impact Evaluation of Alternative Irrigation Technology in Crete: Correcting for Selectivity Bias," Environmental & Resource Economics, Springer;European Association of Environmental and Resource Economists, vol. 79(3), pages 551-574, July.
    8. German Blanco, 2017. "Who benefits from job placement services? A two-sided analysis," Journal of Productivity Analysis, Springer, vol. 47(1), pages 33-47, February.
    9. Centorrino, Samuele & Perez Urdiales, Mari­a & Bravo-Ureta, Boris & Wall, Alan, 2021. "Binary Endogenous Treatment in Stochastic Frontier Models with an Application to Soil Conservation in El Salvador," 95th Annual Conference, March 29-30, 2021, Warwick, UK (Hybrid) 312058, Agricultural Economics Society - AES.
    10. Ayobami Adetoyinbo & Verena Otter, 2022. "Can producer groups improve technical efficiency among artisanal shrimpers in Nigeria? A study accounting for observed and unobserved selectivity," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 10(1), pages 1-33, December.
    11. Begin, Rosemarie & Tamini, Lota D. & Doyon, Maurice, 2014. "L'effet du travail hors-ferme sur l'efficacité technique des fermes laitières québécoises: un modèle intégrant les biais de sélection sur les observables et inobservables," Working Papers 187233, University of Laval, Center for Research on the Economics of the Environment, Agri-food, Transports and Energy (CREATE).
    12. Wang, Anbang & He, Ke & Zhang, Junbiao & Zeng, Yangmei, 2021. "Green Production Technologies and Technical Efficiency of Rice Farmers in China: A Case Study of Straw-Derived Biochar," 2021 Conference, August 17-31, 2021, Virtual 315026, International Association of Agricultural Economists.
    13. Bravo-Ureta, Boris E. & Jara-Rojas, Roberto & Lachaud, Michee A. & Moreira L., Victor H. & Scheierling, Susanne M., 2015. "Water and Farm Efficiency: Insights from the Frontier Literature," 2015 AAEA & WAEA Joint Annual Meeting, July 26-28, San Francisco, California 206076, Agricultural and Applied Economics Association.
    14. Pieri, Fabio & Vecchi, Michela & Venturini, Francesco, 2018. "Modelling the joint impact of R&D and ICT on productivity: A frontier analysis approach," Research Policy, Elsevier, vol. 47(9), pages 1842-1852.
    15. William C. Horrace & Hyunseok Jung, 2018. "Stochastic frontier models with network selectivity," Journal of Productivity Analysis, Springer, vol. 50(3), pages 101-116, December.
    16. Khalid Maman Waziri, 2017. "Generalized Glass Ceilings in the United States – A Stochastic Metafrontier Approach," Working Papers halshs-01569834, HAL.
    17. Musa Hasen Ahmed & Kumilachew Alamerie Melesse, 2018. "Impact of off-farm activities on technical efficiency: evidence from maize producers of eastern Ethiopia," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 6(1), pages 1-15, December.
    18. Junaedi, Mohammad & Daryanto, Heny Kuswanti Suwarsinah & Sinaga, Bonar Marulitua & Hartoyo, Sri, 2016. "Technical Efficiency And The Technology Gap In Wetland Rice Farming In Indonesia: A Metafrontier Analysis," International Journal of Food and Agricultural Economics (IJFAEC), Alanya Alaaddin Keykubat University, Department of Economics and Finance, vol. 4(2), pages 1-12, April.
    19. Roy, Manish & Mazumder, Ritwik, 2016. "Technical Efficiency of Fish Catch in Traditional Fishing: A Study in Southern Assam," Journal of Regional Development and Planning, Rajarshi Majumder, vol. 5(1), pages 55-68.
    20. Roberto Furesi & Fabio Madau & Pietro Pulina, 2013. "Technical efficiency in the sheep dairy industry: an application on the Sardinian (Italy) sector," Agricultural and Food Economics, Springer;Italian Society of Agricultural Economics (SIDEA), vol. 1(1), pages 1-11, December.

    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

    Statistics

    Access and download statistics

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:kap:sbusec:v:59:y:2022:i:2:d:10.1007_s11187-021-00554-w. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Sonal Shukla or Springer Nature Abstracting and Indexing (email available below). General contact details of provider: http://www.springer.com .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.