**Appendix A**

**Table A1.** The Modified Jones model (1).

**Notes:** The equation for the Modified Jones model developed by Dechow et al. (1995): TAit Ait−1 = β0 + β1 1 Ait−1 + β2 ΔREVit−ΔRECit Ait−1 + β3 PPEit Ait−1 + εit(1). Standard errors in parentheses, \*\*\* *p* < 0.01.

**Table A2.** The performance matched model (2).

**Notes:** The equation for the performance matched model by Kothari et al. (2005): TAit Ait−1 = β0 + β1 1 Ait−1 + β2 ΔREVit−ΔRECit Ait−1 + β3 PPEit Ait−1 + β4 ROAit Ait−1 + εit(2). Standard errors in parentheses, \*\*\* *p* < 0.01, \*\* *p* < 0.05.

**Notes:** Test of H0: difference in coefficients is not systematic. The random effects estimator is chosen if the *p*-value is > 0.05, and the fixed effect estimator is chosen otherwise.

**Table A4.** Correlation matrix.

**Notes:** According to Brooks (2019) a correlation between two variables that exceeds 0.80 indicates severe multicollinearity. The variables are defined as: abs\_DA = absolute value of discretionary accruals, BISE = board independence, DER = employee representatives, SOD = share ownership by directors, MJS = directors as majority shareholders, BA = board activity, AC = audit committee, FS = firm size, ROA = return on assets, ROE = return on equity.

**Notes:** According to Brooks (2019) a VIF index over five indicates severe multicollinearity. The variables are defined as: abs\_DA = absolute value of discretionary accruals, BISE = board independence, DER = employee representatives, SOD = share ownership by directors, MJS = directors as majority shareholders, BA = board activity, AC = audit committee, FS = firm size, ROA = return on assets, ROE = return on equity.

**Table A6.** Regression results of model (3) without corporate governance variables.

**Notes:** The equation used to test the robustness: absDAit = β0 + β1(FSit) + β2(ROAit) + β3(ROEit) + εit(3). \*\*\* and \* indicate the significance level at 1% and 10%, respectively (two-tailed). All numbers reported in NOK million. Robust standard errors in parentheses, \*\*\* *p* < 0.01, \* *p* < 0.1

#### **References**

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**1. Introduction**

After the great economic and financial crisis of 2007, the Covid-19 pandemic is posing one of the greatest economic and social challenges humans have ever faced. From a health and humanitarian point of view, the extent of this impact has already been understood by everyone, thanks to the daily data attesting to a continuing pandemic spread throughout the world, even as procedures aimed at combating the virus have been launched. As far as the social and economic effects are concerned, direct understanding has come about as a result of regulatory impositions that have restricted not only transport and travel but also economic activities, inevitably slowing down the entire economic system.

But can we claim to have fully understood the effects of this scale on companies and business?

This new climate in which both businesses and private citizens have found themselves can really be considered as unexplored, although there have been similar experiences in the last century [1]. The pandemic has induced profound negative effects throughout the global economy [2] affecting mainly the key points of companies' operational management—first of all the supply chain—together with the way companies have to relate to clients and consumers. Initially, the negative influences led to the closure of simple units, then of business units, and finally, for the companies in trouble, of the entire business. This was induced by multiple factors:

All this was accompanied by the payment of extraordinary costs for adjustments to business structures required by governments to continue operations using precautionary health measures to contain the pandemic spread.

Therefore, the costs that companies have had to bear have paradoxically increased, but at the same time sales volumes and capacity utilization have decreased.

According to CERVED data, nearly one in four businesses closed at a loss in the first year of the pandemic. The study shows that in 2020, the revenues of the companies in the sample analyzed collapsed by 10.7%, compared with the 3% growth they had achieved in 2019, the last year of a positive trend that began in 2013 [3].

**Figure 1.**

*Scenarios induced by the pandemic crisis and the revolutionary process 4.0. Source: author's elaboration.*

#### *Companies and Covid-19: Emerging Challenges and Recovery Strategies through Technological… DOI: http://dx.doi.org/10.5772/intechopen.102960*

These irrefutable data make it possible to outline and hypothesize new trends and possible scenarios induced by the pandemic (**Figure 1**).

The inability to individually ensure the smooth running of business operations and the provision of required services will increasingly induce companies to operate collegially in a network rather than continue with individualistic models. The perception of common problems will lead to the creation of business alliances aimed at stabilizing supply chains and formulating new models capable of achieving cost efficiency and maximizing performance.

However, this need will be realized in an increasing context of digitization. The impressive digital framework already underway will become so large that it will be able to absorb most of the investments made by companies to satisfy the new desires and upcoming needs of customers who are increasingly operating on the Internet.

Therefore, we are driven to argue that all those companies now considered *traditional* that, by inability or desire, will not want to adapt to such a change will be inexorably expelled from a system of advanced interconnection and operation 4.0.