IDEAS home Printed from https://ideas.repec.org/a/eee/proeco/v234y2021ics0925527321000517.html
   My bibliography  Save this article

Impact of COVID-19 outbreak on employee performance – Moderating role of industry 4.0 base technologies

Author

Listed:
  • Narayanamurthy, Gopalakrishnan
  • Tortorella, Guilherme

Abstract

COVID-19 outbreak has implied significant changes in the way service organizations work, affecting employees' routine and activities. At the same time, the advent of Industry 4.0 (I4.0) introduced new technologies that might facilitate such activities, mitigating the COVID-19's implications. The objective of this research is two-fold. First, we aim at examining the impact of COVID-19's work implications on employees' performance (i.e. output quality and delivery). Second, we seek to verify the moderating role of I4.0 base technologies on this relationship. We surveyed 106 employees of different service organizations who have been working remotely during the pandemic and analyzed their responses through multivariate techniques. Results revealed that COVID-19's work implications (i.e. home office work environment, job insecurity and virtual connection) do impact employee's performance, although not at the same extent. Further, we found that I4.0 technologies moderate the enhancement of employee's performance. However, the orientation and intensity of such moderation may vary according to the performance metric and work implication under analysis. As COVID-19 outbreak inevitably pushed new ways of working that can become an integral part of the post-pandemic world, our research provides important theoretical and practical implications for improving employee's performance through the digitalization of service organizations.

Suggested Citation

  • Narayanamurthy, Gopalakrishnan & Tortorella, Guilherme, 2021. "Impact of COVID-19 outbreak on employee performance – Moderating role of industry 4.0 base technologies," International Journal of Production Economics, Elsevier, vol. 234(C).
  • Handle: RePEc:eee:proeco:v:234:y:2021:i:c:s0925527321000517
    DOI: 10.1016/j.ijpe.2021.108075
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0925527321000517
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.ijpe.2021.108075?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. Tava Lennon Olsen & Brian Tomlin, 2020. "Industry 4.0: Opportunities and Challenges for Operations Management," Manufacturing & Service Operations Management, INFORMS, vol. 22(1), pages 113-122, January.
    2. Julian Marius Müller & Daniel Kiel & Kai-Ingo Voigt, 2018. "What Drives the Implementation of Industry 4.0? The Role of Opportunities and Challenges in the Context of Sustainability," Sustainability, MDPI, vol. 10(1), pages 1-24, January.
    3. Armstrong, J. Scott & Overton, Terry S., 1977. "Estimating Nonresponse Bias in Mail Surveys," MPRA Paper 81694, University Library of Munich, Germany.
    4. Dmitry Ivanov & Alexandre Dolgui & Boris Sokolov, 2019. "The impact of digital technology and Industry 4.0 on the ripple effect and supply chain risk analytics," International Journal of Production Research, Taylor & Francis Journals, vol. 57(3), pages 829-846, February.
    5. Anastasios D. Diamantidis & Prodromos Chatzoglou, 2018. "Factors affecting employee performance: an empirical approach," International Journal of Productivity and Performance Management, Emerald Group Publishing Limited, vol. 68(1), pages 171-193, December.
    6. Andrew Kusiak, 2020. "Open manufacturing: a design-for-resilience approach," International Journal of Production Research, Taylor & Francis Journals, vol. 58(15), pages 4647-4658, July.
    7. Christian Arnold & Daniel Kiel & Kai-Ingo Voigt, 2016. "How The Industrial Internet Of Things Changes Business Models In Different Manufacturing Industries," International Journal of Innovation Management (ijim), World Scientific Publishing Co. Pte. Ltd., vol. 20(08), pages 1-25, December.
    8. Jill E. Hobbs, 2020. "Food supply chains during the COVID‐19 pandemic," Canadian Journal of Agricultural Economics/Revue canadienne d'agroeconomie, Canadian Agricultural Economics Society/Societe canadienne d'agroeconomie, vol. 68(2), pages 171-176, June.
    9. Frank, Alejandro Germán & Dalenogare, Lucas Santos & Ayala, Néstor Fabián, 2019. "Industry 4.0 technologies: Implementation patterns in manufacturing companies," International Journal of Production Economics, Elsevier, vol. 210(C), pages 15-26.
    10. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    11. Tortorella, Guilherme Luz & Miorando, Rogério & Marodin, Giuliano, 2017. "Lean supply chain management: Empirical research on practices, contexts and performance," International Journal of Production Economics, Elsevier, vol. 193(C), pages 98-112.
    12. Guilherme Luz Tortorella & Diego Fettermann, 2018. "Implementation of Industry 4.0 and lean production in Brazilian manufacturing companies," International Journal of Production Research, Taylor & Francis Journals, vol. 56(8), pages 2975-2987, April.
    13. Frank L Forcino & Lindsey R Leighton & Pamela Twerdy & James F Cahill, 2015. "Reexamining Sample Size Requirements for Multivariate, Abundance-Based Community Research: When Resources are Limited, the Research Does Not Have to Be," PLOS ONE, Public Library of Science, vol. 10(6), pages 1-18, June.
    14. Marodin, Giuliano & Frank, Alejandro Germán & Tortorella, Guilherme Luz & Netland, Torbjørn, 2018. "Lean product development and lean manufacturing: Testing moderation effects," International Journal of Production Economics, Elsevier, vol. 203(C), pages 301-310.
    15. Moacir Godinho Filho & Gilberto Miller Devós Ganga & Angappa Gunasekaran, 2016. "Lean manufacturing in Brazilian small and medium enterprises: implementation and effect on performance," International Journal of Production Research, Taylor & Francis Journals, vol. 54(24), pages 7523-7545, December.
    Full references (including those not matched with items on IDEAS)

    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. Tortorella, Guilherme Luz & Saurin, Tarcísio Abreu & Filho, Moacir Godinho & Samson, Daniel & Kumar, Maneesh, 2021. "Bundles of Lean Automation practices and principles and their impact on operational performance," International Journal of Production Economics, Elsevier, vol. 235(C).
    2. Sunder M, Vijaya & Prashar, Anupama, 2024. "The interplay of lean practices and digitalization on organizational learning systems and operational performance," International Journal of Production Economics, Elsevier, vol. 270(C).
    3. Tortorella, Guilherme Luz & Narayanamurthy, Gopalakrishnan & Thurer, Matthias, 2021. "Identifying pathways to a high-performing lean automation implementation: An empirical study in the manufacturing industry," International Journal of Production Economics, Elsevier, vol. 231(C).
    4. Oliveira-Dias, Diéssica de & Maqueira-Marin, Juan Manuel & Moyano-Fuentes, José & Carvalho, Helena, 2023. "Implications of using Industry 4.0 base technologies for lean and agile supply chains and performance," International Journal of Production Economics, Elsevier, vol. 262(C).
    5. Tortorella, Guilherme Luz & Cawley Vergara, Alejandro Mac & Garza-Reyes, Jose Arturo & Sawhney, Rapinder, 2020. "Organizational learning paths based upon industry 4.0 adoption: An empirical study with Brazilian manufacturers," International Journal of Production Economics, Elsevier, vol. 219(C), pages 284-294.
    6. Dubey, Rameshwar & Gunasekaran, Angappa & Childe, Stephen J. & Bryde, David J. & Giannakis, Mihalis & Foropon, Cyril & Roubaud, David & Hazen, Benjamin T., 2020. "Big data analytics and artificial intelligence pathway to operational performance under the effects of entrepreneurial orientation and environmental dynamism: A study of manufacturing organisations," International Journal of Production Economics, Elsevier, vol. 226(C).
    7. Li, Ying & Dai, Jing & Cui, Li, 2020. "The impact of digital technologies on economic and environmental performance in the context of industry 4.0: A moderated mediation model," International Journal of Production Economics, Elsevier, vol. 229(C).
    8. Xi, Mengjie & Liu, Yang & Fang, Wei & Feng, Taiwen, 2024. "Intelligent manufacturing for strengthening operational resilience during the COVID-19 pandemic: A dynamic capability theory perspective," International Journal of Production Economics, Elsevier, vol. 267(C).
    9. Soliman, Marlon & Saurin, Tarcisio Abreu & Anzanello, Michel Jose, 2018. "The impacts of lean production on the complexity of socio-technical systems," International Journal of Production Economics, Elsevier, vol. 197(C), pages 342-357.
    10. Tao, Zhibin & Chao, Jiaxiao, 2024. "Unlocking new opportunities in the industry 4.0 era, exploring the critical impact of digital technology on sustainable performance and the mediating role of GSCM practices," Innovation and Green Development, Elsevier, vol. 3(3).
    11. Dwivedi, Ashish & Moktadir, Md. Abdul & Chiappetta Jabbour, Charbel José & de Carvalho, Daniel Estima, 2022. "Integrating the circular economy and industry 4.0 for sustainable development: Implications for responsible footwear production in a big data-driven world," Technological Forecasting and Social Change, Elsevier, vol. 175(C).
    12. Bag, Surajit & Gupta, Shivam & Kumar, Sameer, 2021. "Industry 4.0 adoption and 10R advance manufacturing capabilities for sustainable development," International Journal of Production Economics, Elsevier, vol. 231(C).
    13. Zhao, Nanyang & Hong, Jiangtao & Lau, Kwok Hung, 2023. "Impact of supply chain digitalization on supply chain resilience and performance: A multi-mediation model," International Journal of Production Economics, Elsevier, vol. 259(C).
    14. Eslami, Mohammad H. & Achtenhagen, Leona & Bertsch, Cedric Tobias & Lehmann, Annika, 2023. "Knowledge-sharing across supply chain actors in adopting Industry 4.0 technologies: An exploratory case study within the automotive industry," Technological Forecasting and Social Change, Elsevier, vol. 186(PA).
    15. Culot, Giovanna & Orzes, Guido & Sartor, Marco & Nassimbeni, Guido, 2020. "The future of manufacturing: A Delphi-based scenario analysis on Industry 4.0," Technological Forecasting and Social Change, Elsevier, vol. 157(C).
    16. Li, Zhongshun & Xie, Weihong & Wang, Zhong & Wang, Yongjian & Huang, Danyu, 2023. "Antecedent configurations and performance of business models of intelligent manufacturing enterprises," Technological Forecasting and Social Change, Elsevier, vol. 193(C).
    17. Tortorella, Guilherme Luz & Fogliatto, Flavio S. & Cauchick-Miguel, Paulo A. & Kurnia, Sherah & Jurburg, Daniel, 2021. "Integration of Industry 4.0 technologies into Total Productive Maintenance practices," International Journal of Production Economics, Elsevier, vol. 240(C).
    18. Virmani, Naveen & Sharma, Shikha & Kumar, Anil & Luthra, Sunil, 2023. "Adoption of industry 4.0 evidence in emerging economy: Behavioral reasoning theory perspective," Technological Forecasting and Social Change, Elsevier, vol. 188(C).
    19. Tortorella, Guilherme Luz & Saurin, Tarcisio A. & Hines, Peter & Antony, Jiju & Samson, Daniel, 2023. "Myths and facts of industry 4.0," International Journal of Production Economics, Elsevier, vol. 255(C).
    20. Huang, Kerry & Wang, Kedi & Lee, Peter K.C. & Yeung, Andy C.L., 2023. "The impact of industry 4.0 on supply chain capability and supply chain resilience: A dynamic resource-based view," International Journal of Production Economics, Elsevier, vol. 262(C).

    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:eee:proeco:v:234:y:2021:i:c:s0925527321000517. 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: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/locate/ijpe .

    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.