A Configuration Approach to Reduce the Risk of COVID-19 Employees Infection in the Manufacturing Firms: The Role of Machine Automatization

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.

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