Published at : 07 Oct 2022
Volume : IJtech Vol 13, No 4 (2022)
DOI : https://doi.org/10.14716/ijtech.v13i4.5287
|Jorge Heredia||Department of Business Administration, Universidad del Pacífico, Calle Sanchez Cerro 2141, Jesús María, Lima 11, Perú|
|Cristian Geldes||Faculty of Economics and Business, Universidad Alberto Hurtado. Erasmo Escala 1835. Oficina 206, Santiago. Chile|
|Alejandro Flores||Department of Business Administration, Universidad del Pacífico, Calle Sanchez Cerro 2141, Jesús María, Lima 11, Perú|
|Walter Heredia||Facultad de Economía y Negocios, Universidad del Desarrollo, Santiago, Chile|
|Felix M Carbajal Gamarra||Energy Engineering, University of Brasilia, FGA-UnB, St. Leste Projeção A - Gama Leste, Brasilia 72444-240, DF, Brazil|
|Luisa Miranda||Pontificia Universidad Católica de Chile, CEO Nextmedicall, Jr. Domingo Ponte 1171, Lima, Perú|
Does automation adoption mitigate the COVID-19 infection rate of employees? What resources and internal and external factors need to be configured with automation to mitigate COVID-19 contagion from employees successfully? According to the type of automation. What resources efficiently complement to mitigate the contagion rate from employers? From a fuzzy-set qualitative comparative analysis (fsQCA) approach, we analyzed 759 manufacturing firms in Finland, drawn from the World Bank 2020 Enterprise Survey; this study addresses the multiple configurations that drive pandemic risk mitigation and management. We find that configurations under automation reduce the risk of employee infection. Our results show the critical role of automation in employee safety. We argue that access to government support and the development of technological innovation are necessary conditions for implementing measures to prevent and mitigate the risk of contagion in the employee. In addition, the first configuration states that manufacturing firms employing soft automation can successfully mitigate employee exposure. The second configuration states that high human resource flexibility successfully complements firms with complex automation to achieve high mitigation. Finally, the third configuration shows those manufacturing firms that employ low-tech automation (manual processes); in this manner, digitization enables successfully mitigating pandemic contagion. Moreover, it suggests recommendations for policymakers and managers.
COVID-19; Digitalization; fsQCA; Industry 4.0; Machine Automatization
The death rate due to COVID-19 has increased already to three million people (Agus et al., 2021). Therefore, it is essential to know what strategies firms should implement to mitigate employee infection for welfare and safety in this "new normal." In such a manner, as resilient firms return to their activities, they must establish new safety and welfare measures for workers to mitigate the pandemic risk. Therefore, having better work conditions through high levels of safety and adequate worker health in a company plays a fundamental role (Levy et al., 2017; Berawi, 2021).
To achieve this purpose, Seale et al. (2020) state that physical distancing, use of masks, and hand hygiene, persist in being considered essential to deal with the pandemic. Therefore, firms present an essential role in caring for the welfare of employees who face high exposure to the virus they perform in essential activities (Rothan & Byraredde, 2020).
Currently, in the era of Industry 4.0 (I4.0), technological advances, such as Artificial Intelligence (A.I.) and automation, could play a key role in mitigating the infection of employees by COVID-19. In such a manner, automation processes generate greater interest in industries because it offers an opportunity for jobs without much contact with other people, drastically decreasing infections.
However, what conditions automation and digitization will reduce employee contagion remains unclear. Thus, the present study attempts to fill this gap by interacting with internal and external variables to understand the complexity and explain risk mitigation in this "new normal." In this sense, we address these challenges to develop an empirical model that seeks to explain the best practice strategies that allow high-risk mitigation in workers from a business perspective. So far, few studies seek to understand the mechanisms that lead companies to adopt risk mitigation measures (De Bruin et al., 2020; Koonin, 2020).
In addition, we seek to know the role of automation, so our research aims to fill this gap, provide good practices to companies, and work together with policymakers in this "new normality." We believe the automation variable alone does not mitigate contagions for the safety of workers. In this sense, we consider it essential to know which resources successfully complement each type of automation to mitigate the contagions in the workers of manufacturing companies. Thus, our objective is twofold. Firstly, to identify which factors lead to high-risk mitigation to build resilience that provides a better quality of life for workers and anticipate problems in the short term. Secondly, we seek to know the interactions of the factors that explain our objective. Third, analyze what type of automation is complemented by resources that could reduce the rate of contagion in employees.
Therefore, this study addressed two questions: (i) How do these factors interact, and under what context do they improve worker safety and mitigate risk during the pandemic? (ii) What type of automation improves worker's safety in developed manufacturing firms? According to the type of automation (iii), What are resources that efficiently complement to mitigate the contagion rate from employers? We employ an asymmetric methodology such as fuzzy-set qualitative analysis (fsQCA) to achieve our objective. It analyzes multiple causality and equifinality.
The research is structured as follows: a theoretical framework addressing the antecedents of firms with developed economies, the formulation of hypotheses, and developing of a proposed model. In addition, the presentation of the method and the results. Finally, we state the conclusions and give a discussion, respectively.
The present study explores how to overcome employee safety and risk mitigation during the COVID-19 pandemic. In such a manner, we know that workers' safety, health, and welfare have become the focus of attention to analyze during the pandemic. However, our study seeks to propose the roles of automation and technology in manufacturing firms through new strategies and tools to prevent and mitigate the risk of infections in employees. In conclusion, automation is essential in strategies to prevent and mitigate worker infections. In addition, our study contributes to knowing the set of resources that successfully complement each other in manufacturing firms according to each type of automation, thus exploring the companies' capabilities in managing strategies depending on the company's decisions. According to our results, successfully digitalization complements companies that use a low level of automation (manual processes) to jointly generate preventive measures for workers' safety. Finally, we propose the need for a relationship between business and government to mitigate the pandemic risk. In addition, we provide practical implications for managers to look at the internal factors (resources and capabilities) that mitigate employee infection.
The support at the Research Center of Universidad
del Pacífico (CIUP) is gratefully acknowledged. We also thank Jorge Peña
Contreras for his support of data processing.
Agus, A.A., Yudoko, G., Mulyono, N., Imaniya, T., 2021. E-Commerce Performance, Digital Marketing Capability and Supply Chain Capability within E-Commerce Platform: Longitudinal Study Before and After COVID-19. International Journal of Technology, Volume 12(2), p. 360
Andrews, D., Criscuolo, C., Gal, P.N., 2016. The Best Versus the Rest: The Global Productivity Slowdown, Divergence Across Firms and the Role of Public Policy
Berawi, M.A., 2021. Innovative Technology for Post-Pandemic Economic Recovery. International Journal of Technology. Volume 12(1), pp. 1–4
Chuah, S.H.W., Aw, E.C.X., Yee, D., 2021. Unveiling the Complexity of Consumers' Intention to Use Service Robots: An FsQCA Approach. Computers in Human Behavior, Volume 123, p. 106870
De Bruin, Y.B., Lequarre, A.S., McCourt, J., Clevestig, P., Pigazzani, F., Jeddi, M.Z., Colosio, C. and Goulart, M., 2020. Initial Impacts of Global Risk Mitigation Measures Taken During the Combatting of the COVID-19 Pandemic. Safety science, Volume 128, p. 104773
De Souza, A.F., Martins, J., Maiochi, H., Juliani, A.D.P., Jaskowiak, P.A., 2020. Development of a Mobile Application for Monitoring and Controlling a CNC Machine Using Industry 4.0 Concepts. The International Journal of Advanced Manufacturing Technology, Volume 111(9), pp. 2545–2552
Dennerlein, J.T., Burke, L., Sabbath, E.L., Williams, J.A., Peters, S.E., Wallace, L., Karapanos, M. and Sorensen, G., 2020. An Integrative Total Worker Health Framework for Keeping Workers Safe and Healthy During the COVID-19 Pandemic. Human factors, Volume 62(5), pp. 689–696
Fiss, P.C., 2011. Building Better Causal Theories: A Fuzzy Set Approach to Typologies in Organization Research. Academy of Management Journal, Volume 54(2), pp. 393–420
Gonçalves, T., Gaio, C., Silva, M., 2018. Target Costing and Innovation-Exploratory Configurations: A Comparison of FsQCA, Multivariate Regression, and Variable Cluster Analysis. Journal of Business Research, Volume 89, pp. 378–384
Heredia, J., Castillo-Vergara, M., Geldes, C., Gamarra, F.M.C., Flores, A., Heredia, W., 2022. How Do Digital Capabilities Affect Firm Performance? The Mediating Role of Technological Capabilities in the “New Normal”. Journal of Innovation & Knowledge, Volume 7(2), p. 100171
Hurtado, L.A., Mocanu, E., Nguyen, P.H., Gibescu, M., Kling, W.L., 2015. Comfort-constrained Demand Flexibility Management for Building Aggregations Using a Decentralized Approach. In 2015 International Conference on Smart Cities and Green ICT Systems, pp. 1–10
Kavitha, B.C., Vallikannu, R., 2022. IoT Assisted Predictive Maintenance and Worker Safety: An Initiative. In Information and Communication Technology for Competitive Strategies (ICTCS 2020). Springer, Singapore, pp. 719–727
Koonin, L.M., 2020. Novel Coronavirus Disease (COVID-19) Outbreak: Now is the Time to Refresh Pandemic Plans. Journal of business continuity & emergency planning, Volume 13(4), pp. 298–312
Levy, B.S., Wegman, D.H., Baron, S.L., Sokas, R.K., 2017. Occupational and Environmental Health: Recognizing and Preventing Disease and Injury (7th ed.). Oxford University Press
Liao, Y., Deschamps, F., Loures, E.D.F.R., Ramos, L.F.P., 2017. Past, Present and Future of Industry 4.0-a Systematic Literature Review and Research Agenda Proposal. International journal of production research, Volume 55(12), pp. 3609–3629
Manzo, M.A., Rodríguez, J.C., 2022. Ensuring the High Performance of Design and Engineering Firms in Mexico's Aerospace Industry: A Qualitative Comparative Analysis. International Journal of Technology. Volume 13(1), pp. 5–15
Mithani, M.A., 2020. Adaptation in the Face of the New Normal. Academy of Management Perspectives, 34(4), 508–530
Naruetharadhol, P., Srisathan, W.A., Gebsombut, N., Wongthahan, P., Ketkaew, C., 2022. Industry 4.0 for Thai SMEs: Implementing Open Innovation as Innovation Capability Management. International Journal of Technology. Volume 13(1), pp. 48–57
Ragin, C.C., 2000. Fuzzy-set Social Science. University of Chicago Press
Rihoux, B., Ragin, C.C., Yamasaki, S., Bol, D., 2009. Conclusions-The Way (s) Ahead. Configurational comparative methods: Qualitative comparative analysis (QCA) and related techniques, pp. 167–178
Rothan, H.A., & Byrareddy, S.N., 2020. The Epidemiology and Pathogenesis of Coronavirus Disease (COVID-19) Outbreak. Journal of autoimmunity, Volume 109, p. 102433
Schneider, C.Q., Wagemann, C., 2012. Set-theoretic Methods for the Social Sciences: A Guide to Qualitative Comparative Analysis. Cambridge University Press
Seale, H., Heywood, A.E., Leask, J., Sheel, M., Thomas, S., Durrheim, D.N., Bolsewicz, K. and Kaur, R., 2020. COVID-19 is Rapidly Changing: Examining Public Perceptions and Behaviors in Response to This Evolving Pandemic. PloS one, Volume 15(6), p. e0235112
Stroe, S., Sirén, C., Parida, V., Wincent, J., 2022. Framing Ideas for New Venture Resources Acquisition in Crises: An fsQCA Analysis. Journal of Business Venturing Insights, Volume 17, p. e00307
Szajna, A., & Kostrzewski, M. (2022). AR-AI Tools as a Response to High Employee Turnover and Shortages in Manufacturing during Regular, Pandemic, and War Times. Sustainability, 14(11), 6729. Volume 14(11), pp. 6729
Tiirinki, H., Tynkkynen, L.K., Sovala, M., Atkins, S., Koivusalo, M., Rautiainen, P., Jormanainen, V. and Keskimäki, I., 2020. COVID-19 Pandemic in Finland–Preliminary Analysis on Health System Response and Economic Consequences. Health policy and technology, Volume 9(4), pp. 649–662
Zheng, P., Sang, Z., Zhong, R.Y., Liu, Y., Liu, C., Mubarok, K., Yu, S. and Xu, X., 2018. Smart Manufacturing Systems for Industry 4.0: Conceptual Framework, Scenarios, and Future Perspectives. Frontiers of Mechanical Engineering, Volume 13(2), pp. 137–150