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Sailing in the Storm in 2020, The Business to Consumer Case


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International Journal of Management Science and Business Administration
Volume 7, Issue 2, January 2021, Pages 20-27

Sailing in the Storm in 2020, The Business to Consumer Case

DOI: 10.18775/ijmsba.1849-5664-5419.2014.72.1003
URL: https://doi.org/10.18775/ijmsba.1849-5664-5419.2014.72.1003

Manu Viorica Mădălina

The Bucharest University of Economic Studies – Council for Doctoral Studies (CSUD), Bucharest, Romania

Abstract: This chapter analyses factors that add value to the businesses in 2020, especially the new businesses from the digital economy. Many Romanian companies fight hard to find their way to success (or survival) in the absence of one valuable objective of the business and in this chapter we will analyze them. This chapter researches the enterprise value and its determinants, in order to understand the ways to increase the company value, while considering Jensen’s theory of value maximization as the single important objective of a company.

Keywords: Crisis, Ecommerce, Firm value, Panel data, Listed companies.

Sailing in the Storm in 2020, The Business to Consumer Case

1. Introduction

The factors of the value of the business in 2020 are the main concerns of this chapter, considering that many businesses are still ‘seeking a lifeline to save the business’ as Ed Kaplan stated; they are struggling, not certain that they will be able to continue the activity. In this paper, we show that the firm value (or the market cap, which is a proxy value) depends on many factors, including the economic and financial stability.

According to professor Jensen’s theory (2002), it is impossible to maximize in more than one dimension (Jensen, 2002). When implementing organizational change, the aim should be the increase in the long-term market value of the firm (MV), which is ‘the sum of the values of all financial claims on the firm, including equity, debt, preferred stock, and warrants’ (Jensen, 2002).

The enterprise value (EV) approximates current market value (MV) of a company to determine takeover or merger price of a firm; unlike Market Cap, EV takes into account: the entire liquid asset, outstanding debt, and exotic equity instruments (from the balance sheet). When takeover occurs, the parent company will have to assume the target company’s liabilities but will take possession of all cash and cash equivalents.

When analyzing successful e-commerce companies’ objectives, we come to agree that they find success when they focus on a single value driven objective for the long term. Difficulties rise when companies waste their efforts and limited resources on too many (unsustainable) objectives for the short term. The company’s strategy is, therefore, of utmost importance.

1.1 The Context of the Year 2020

The COVID-19 pandemic in 2020 has affected companies, forcing them to find solutions to significantly increased uncertainty, blocking supply and demand, financial market volatility and notable adjustments in financial asset prices (NBR, 2020). The pandemic (CO-VID) highlighted the vulnerabilities of companies, forcing them to restructure and find solutions to survive / maximize the value of the company.

In a study, Guda (2020) states that 55% of the active Romanian business environment needs help due to the pandemic, due to the blockage of economic activities, or the decrease of demand. Although the spread of this pandemic is lower than in recent ones, the financial shocks were significant and the stock market capitalization of companies decreased by about $ 27 trillion in the first quarter of 2020. Compared to previous financial crises, such as oil (1970), IT companies Banking stemmed from the deficiencies accumulated in certain sectors of activity, which spread to other sectors of activity, the CO-VID pandemic severely affected most sectors in 2020. The Global Financial Crisis of 2007-2009 has been described as the most severe, unpredictable, complex, systemic international crisis since the Great Depression (early 1930s) (Malliaris, Shaw, and Shefrin, 2016).

According to PwC’s EMEA Private Business Survey 2020, ‘51% of private business surveyed are expecting revenues to decline in 2020 and afterwards, 57% of agility champions have trained and upskilled their staff, 75% of agility champions see new technologies as key to transformation and 81% of agility champions say sustainable practices are critical in the new COVID-19 normal’.

Turbulence is erratic and unpredictable says Friedrich von Metzler, but we must handle it. When we pay attention to early signs, “they give business leaders an outstanding map for how to successfully navigate a company through crises.” (Friedrich von Metzler)  “Turbulence and unpredictability are inevitable. We are in truly uncharted waters, with no good maps. Many difficult decisions managers and leaders need to make in turbulent times” (Peter Schwartz). Stability and predictability vanish and instead, companies are knocked, bounded, and punched by conflicting and relentless forces. And sometimes the turbulence will be so continuous as to plunge the whole economy into a downturn, a recession, or possibly a protracted depression (Philip Kottler).  “With the financial tsunami, Kotler and Caslione keenly proposed a significant new theme — Chaotics — to help design more management and marketing resilience in companies aiming to steer profitably through the turbulence, beyond static equilibrium economic theory to dynamic management and marketing theory” (Professor Taihong Lu).

Advances in technology have improved access to real-time market information and business analytics, improving communication and the integrated management of their operations; improved telecommunications, information management software and personal computing decreased significantly firm’ costs, enabling the creation of efficient business models. Although digital services can substantially expand the reach of businesses, they require massive investments in infrastructure and also, skills. The ‘big data’ are datasets large enough that they cannot be managed or analyzed using typical database management tools (OECD 2015). Data analytics, defined as the use of data storage and process techniques to support decisions are becoming a driver for innovation in a number of scientific areas and are used in collaborative and crowd-based projects. Small and medium-sized enterprises (SMEs), and governments have access now to supercomputing resources. (OECD, 2013)

Automation changes the activities of all sectors, including finance, as robots and computers can perform routine physical work, being increasingly capable of accomplishing activities that include cognitive capabilities. The analysis of the impact of automation, covering 78% of the global labor market, the high percentage of time spent on activities with the technical potential for automation by adapting currently demonstrated technology shows a very high potential for automation, in many countries. In figure 1, we can see the high potential for automation [ ], for the Finance and insurance sector (McKinsey Global Institute, 2018).

Sailing in the Storm in 2020, The Business to Consumer Case

Figure 1: The high potential for automation in the Finance and insurance sector
Source: (McKinsey Global Institute, 2018)

“Disruptive innovation” describes how new entrants target the bottom of a market and then relentlessly move up, ousting established providers by successfully targeting those overlooked segments, frequently at a lower price (Christensen, 2018). For example, companies such as Amazon disrupted entire industries. Innovations, new technologies or business models reshape industries.

The largest companies in 2008 were the oil and gas producers, while in 2018, the top 10 companies in the world were technology companies mainly (Johnston, 2018). The technology giants such as Apple, Google and Facebook have achieved world domination (Figure 2) by investing heavily in developing new products and services leading to an explosion in innovation and faster growth and disrupting established companies.

Sailing in the Storm in 2020, The Business to Consumer Case

Figure 2: The largest companies in 2008 vs. the year 2018
Source: (Johnston, 2018)

The only company remaining in the global top 10 since 2008 is Microsoft, founded in 1975, which doubled its value. Another giant, founded in 1998 by Stanford Ph.D. student, with funding of $41.00M and valuation of $777.96B, Google’s search technologies now connect millions of people with information; similar companies (Figure 2) are Facebook ($2.53B) and Amazon ($16.98M) (CBINSIGHTS, 2019).

When the seller sees that the company is not worth as much as they thought it was should take steps to increase its value. When an e-commerce company, eBay, bought, in 2005, Skype – a small, marginally profitable communications company, for $3.1 billion, the company was worth only $1.7 billion and eBay took a $1.4 billion charge against profits to account for the difference. Then, in 2010, eBay sold 70% of Skype to a group of investors (including the original founders) for $1.9 billion (making Skype worth about $2.7 billion); in 2011, Microsoft bought Skype for $8.5 billion, even though Skype was still at loss (Knight, 2016).

Wruck, Jensen, and Barry (1991) studied a small non-profit firm that almost destroyed itself while trying to maximize over a dozen dimensions at the same time; Cools and van Praag (2000) were the first to formally test the proposition that multiple objectives handicap firms using 80 Dutch firms in the 1993-1997 period, then concluded: “Our findings show the importance of setting one single target for value creation” (Jensen, 2002).

High-tech and innovative unicorns are drivers of development and their high capitalization is indeed justified. The potential and growth opportunities of such companies are the main drivers of their overestimated value. The Unicorn Club is a group of billion-dollar startups with a revolutionary trade brand tagged to their business operations comprises companies under E-commerce sector (17%), Internet Software and Services (14%) and Financial technology (11%).

Digital transformation remains top of the agenda as companies use M&A to add innovative capabilities, improve customer engagement and stays relevant in a changing market; renewables and network assets remain the most attractive acquisition targets and digitization can influence transaction strategies in developed countries (Rennie, 2017).

2. The Method

Using the open-source statistical software “R” and R-studio for analyzing data (Figure 3) is the lingua franca for economists and should get proper attention. The use of R was the focus of several international conferences on official statistics held in Bucharest, Romania and others having business excellence, innovation and sustainability as purpose and so is this paper. In practice, data science teams, economists and corporations use a mix of languages, often, at least R and Python (R for Data Science).

Such free powerful tools allow data import from various sources, including Yahoo finance. The package considered below is a tool for Quantitative Financial Modeling Framework.

Figure 3: Datasets and packages in R
Source: screen shot

The command for installing the package is: install.packages(“quantmod”) .

The following command loads the required packages (fig. 4).

> library(quantmod)

Loading required package: xts

Loading required package: zoo

Sailing in the Storm in 2020, The Business to Consumer Case

Figure 4: Importing the data in R
Source: screen shot

The Financial Services Sector (Yahoo finance) contains 1403 companies listed on NasdaqGS and NYSE and Nasdaq along with information on their market cap and P/E, for which the intraday price was greater than 5 (Currency in USD).

TSIONAS (2019) discusses the advantages of analysing panel data, for it provide more degrees of freedom, however the modeling of heterogeneity cannot be exhausted to fixed and random effect formulations, and slope heterogeneity has to be considered. Dynamic formulations are highly desirable, but they are challenging because of estimation, unit roots and cointegration. Panel data contains certain features of entities over several periods of time (T), contain a cross-sectional dimension (i = 1,..n) and can be (un) balanced  (TSIONAS, 2019):

  • A balanced panel data set includes observations from all possible combinations of the cross-sectional dimension (i = 1,..n) and the time dimension (t = 1, …T), so the total number of observations is N= n x T, while
  • In an unbalanced panel data set, observations are missing and N < n x T and individual time series can differ in length.

The two types of panels are (Voican, 2020):

  • Micro panel – characterized by a large number of individuals (N is in the order of thousands or hundreds) over a short period of time (T = 2 years, maximum 20 years);
  • Macro panel – for an N number of countries (N is usually 7, 20 but also 100), annual variables are observed over a 20-60-year horizon.


2.1 The Research on The Tech Companies

The large technology companies benefit from globalization, accessing new markets and any promising small company that could compete (Figure 5).

Figure 5: The global rise of the ITC companies in 2018 is expected to continue
Source: author’ representation

Despite the fact the company was not reporting profit eMag became part of the Naspers group, in October 2012 and became profitable for the first time in the second half of the 2018. Analyzing the firm potential to grow, we look at Naspers (NPSNY) Book Value/Share of 12.42X, and the relationship between Net Income and Total Asset, Price to Earning of 7.15X.

The quality of the IT and C market/ industry the company competes in is critical indeed as it can push along the company. Usually, the managers that act in the same industry or similar industries tend to replicate the success business models of the front-runners.

Figure 6: Scatterplot of funding and valuation of the unicorns
Source: author’ representation

The Pearsons Correlation Coefficient is one of the most common measures of correlation in financial statistics. For the unicorn companies observed, higher valuation was correlated with higher funding (Pearson’s r is 79, which is normally considered a large effect). In table 1 below, some statistics are included:

Table 5: Descriptive statistics

Valuation ($M)  Total Disclosed Funding ($M)
Mean 3576.841935 Mean 839.3421569
Standard Error 443.0011552 Standard Error 103.7102973
Median 1605.5 Median 376.75
Mode 1000 Mode 500
Standard Deviation 7799.840209 Standard Deviation 1814.189265
Sample Variance 60837507.29 Sample Variance 3291282.687
Kurtosis 51.4314886 Kurtosis 57.15630799
Skewness 6.735592687 Skewness 6.931031008
Range 74000 Range 18565.9
Minimum 1000 Minimum 1
Maximum 75000 Maximum 18566.9
Sum 1108821 Sum 256838.7
Count 310 Count 306


3. Conclusion

The complexity and implications of financial crises require extensive research as it always finds the companies unprepared in front of a crisis. In the actual economy, many Romanian companies fight hard to find their way to success or survival in the absence of one valuable objective of the business. The founders of the companies with little to no credit history do not get good pricing or credit terms being considered risky or “near bankruptcy” – even though the revenue and bank balance clearly show otherwise. The owners that identify such problems and also identify pitching opportunities and proper business partners manage to develop their companies. Companies use M&A to add innovative capabilities, improve customer engagement and staying relevant in a changing market. Although many owners do not feel very comfortable when giving a stake of their company, there are many ways thy can retain their influence/ control of the company.

Even if the theory of value maximization is not so popular, some businesses have found continued success (Ryan Holiday, 2020).


  • (2009). Preface. In D. A. Belsley, E. J. Kontoghiorghes, D. A. Belsley, and E. J. Kontoghiorghes (Eds.), Handbook of Computational Econometrics. Chippenham, Wiltshire: John Wiley and Sons.
  • Belsley, D. A., and Kontoghiorghes, E. J. (2009). Preface. In E. J. David A. Belsley (Ed.), Handbook of Computational Econometrics. Chippenham, Wiltshire: John Wiley and Sons. CrossRef
  • Benjamin S. Baumer, D. T. (2017). Modern Data Science with R. Retrieved 2018, from https://books.google.ro/books?id=NrddDgAAQBAJ&dq=data+science&hl=ro&source=gbs_navlinks_s
  • Briand, G., and Hill, R. C. (2011). Using Excel for Principles of Econometrics. New York: John Wiley and Sons.
  • Brooks, C. (2014). Introductory Econometrics for Finance. Cambridge: Cambridge University Press. CrossRef
  • Dispenza, J. (2019). Distruge-ți obiceiurile nocive (II ed., Vol. 1). București: Curtea veche.
  • Drucker, P. F. (1969). The Age of Discontinuity. Retrieved 12 2018, from https://www.elsevier.com/books/the-age-of-discontinuity/drucker/978-0-434-90395-5
  • Essnet Validat Foundation. (2016, 06). Methodology for data validation 1.0. Retrieved 01 2018, from https://ec.europa.eu/eurostat/cros/system/files/methodology_for_data_validation_v1.o_rev_2016_06_final.pdf
  • Fernandez, P. (2017). Company valuation methods. Madrid.
  • (n.d.). About the Company. (GAMS) Retrieved 12 2017, from https://www.gams.com/about-the-company/
  • Hadley Wickham, W. C. (n.d.). Overview. Retrieved 01 2018, from http://ggplot2.tidyverse.org/
  • Holiday, R. (2017). Perennial Seller: The Art of Making and Marketing Work that Lasts. New York: Penguin Random House LLC.
  • Huber, C. (2017, 11 15). Data management made easy. (STATA) Retrieved 01 2018, from https://blog.stata.com/
  • James Manyika, M. C. (2017, 01). Harnessing automation for a future that works. Retrieved 2018, from https://www.mckinsey.com/global-themes/digital-disruption/harnessing-automation-for-a-future-that-works
  • Loo, M. v. (2017). Statistical Data Cleaning with R. Use of R.
  • Loukides, M. (2011, 04). What Is Data Science?
  • Malliaris, A., Shaw, L., and Shefrin, H. (Eds.). (2016). The Global Financial Crisis and Its Aftermath: Hidden Factors in the Meltdown. New York, USA: Oxford University Press. CrossRef
  • (n.d.). Retrieved 01 2018, from https://de.mathworks.com/videos/matlab-student-overview-89557.html
  • McKinsey and Company. (2017). McKinsey Quarterly. New York.
  • McKinsey Global Institute. (2017, 01). Where machines could replace humans — and where they can’t (yet). Retrieved 2018, from https://public.tableau.com/profile/mckinsey.analytics#!/vizhome/InternationalAutomation/WhereMachinesCanReplaceHumans
  • J.Salkind, e. (2010). Encyclopedia of Research Design. California, USA: SAGE Publications. CrossRef
  • Python Software Foundation. (2018). Retrieved 01 2018, from https://docs.python.org/3/tutorial/index.html
  • Quantitative Micro Software, L. (2007). EViews 6 Student Version.
  • (2009). What is R? Retrieved 01 2018, from https://www.r-statistics.com/page/16/
  • R for Data Science. (n.d.). Retrieved 01 2018, from http://r4ds.had.co.nz/introduction.html
  • R Project R-omania Team. (n.d.). Retrieved from http://www.r-project.ro/
  • (n.d.). Retrieved 01 2018, from https://www.rstudio.com/products/rpackages/
  • (2018). RStudio Desktop. Retrieved 01 2018, from https://www.rstudio.com/products/RStudio/
  • Rudys, T. (2016). Use Of R in Statistics Lithuania. (2).
  • (n.d.). About SAS. (SAS) Retrieved 01 2018, from https://www.sas.com/en_us/company-information.html#history
  • Simionescu, M. (2014). Modelare economică în Matlab, R și Eviews. București: A.S.E.
  • STATA . (n.d.). Why Stata. Retrieved 01 2018.
  • Taleb, N. N. (2010). The Black Swan. Random House Publishing Group.
  • The MathWorks, Inc. (2018). Financial Risk Management: Improving Model Governance with MATLAB.
  • The MathWorks, Inc. (2018). Financial Toolbox. Retrieved 2018.
  • The MathWorks, Inc. (2018). Portfolio Optimization Against a Benchmark.
  • (2018, 02). TIOBE Index for February 2018.
  • (2020, 04). TIOBE Index. Retrieved 04 2020,
  • (2020, 11). TIOBE Index for November 2020.
  • TSIONAS, M. (2019). Panel Data Econometrics: Theory. London, UK: Academic Press.
  • Venables, W. N., Smith, D. M., and Team, R. C. (2016). An Introduction to R.
  • Voican, O. (2020). Using Data Mining Methods to Solve Classification Problems in FinancialBanking Institutions. (1).
  • Wang, Z.-X., and Yao, P.-Y. (2018). Grey Relational Analysis of Economic Policy Uncertainty in Selected European. Economic Computation and Economic Cybernetics Studies and Research, 52(2). CrossRef
  • Webb, A. (2017). McKinsey Quarterly 2017 Number 4: Overview and full issue.
  • Yahoo finance. (n.d.). Financial Services Sector.

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