Attention to COVID-19 –

Attention to COVID-19 –

A content analysis study of Swedish interim reports

Bachelor’s Thesis 15 hp

Department of Business Studies Uppsala University

Fall Semester of 2020

Date of Submission: 2021-01-15

Andreas Ström

Supervisor: Lars Frimanson

Abstract

The purpose of this study was to examine the attention to the COVID-19 pandemic displayed by top level management in companies listed on Nasdaq OMX Stockholm Large Cap, and how this afore- mentioned attention was affected by the board size and board gender diversity of respective company.

To accomplish this, a content analysis on a word-by-word level was conducted of all interim reports produced in 2020 by each company, and data regarding the board size and board gender diversity was gathered, for each company. The frequency with which each company mentioned select keywords concerning the COVID-19 pandemic was measured and then used as a comparative measure of the attention to the COVID-19 pandemic. In order to determine the magnitude of the impact of the independent variables a control variable, firm size, was introduced and linear fits were constructed of various combinations of variables. The resulting fits all clearly displayed an absence of correlation between either board size or board gender diversity and the attention paid to the COVID-19 pan- demic by top level management in large Swedish companies. Hence, this study suggest that there is no increase in board activity regarding daily operations in large Swedish companies during crises.

Keywords: Attention, COVID, Corona, Board Size, Board Gender Diversity, Board Structure, Con-

tent Analysis, Semantic Analysis

Contents

1 Introduction 1

2 Theory 3

2.1 Corporate attention . . . . 3 2.2 Board size . . . . 4 2.3 Board gender diversity . . . . 6

3 Hypothesis development 8

3.1 The impact of board size on corporate attention to crises . . . . 8 3.2 The impact of board gender diversity on corporate attention to crises . . . . 8 3.3 Analytical model . . . . 9

4 Method 10

4.1 General process . . . . 10 4.2 Literature review and Content analysis . . . . 12 4.3 Implementation and Statistical Model . . . . 14

5 Results and Analysis 16

5.1 Analysis omitting the control variable . . . . 17 5.2 Analysis considering the control variable . . . . 21

6 Discussion 25

7 Summary 27

7.1 Conclusion . . . . 27 7.2 Sources of Error and Future Work . . . . 28

References 38

Appendix 39

1 Introduction

At the time of writing, the COVID-19 pandemic has encompassed most of society, had a tremendous impact on many aspects of modern life, and brought with it previously unprecedented issues for hu- manity. Less than a year into the pandemic, there have been 50 million cases and a million deaths (European Centre for Disease Prevention and Control, 2020). The pandemic has unquestionably put a strain on the global economy and society in general, and the resulting situation is one of both new challenges and new possibilities. From an academic viewpoint, the pandemic is a unique possibility to study the effects of a great crisis, which will be the overall aim of this report, in order to further advance the understanding of the effects of crises on modern society. Major questions pertaining to the long-term effects of the pandemic on the economy still remain unanswered today, and most will likely not be definitively answered in the near future. This has, however, not deterred a large amount of research from being conducted as a start to mapping the pandemic’s immediate effects.

Throughout this pandemic it has become evident that attention to and management of crises can be a crucial trait for companies to possess, not only for their own sake, but also for society as a whole.

The modern literature pertaining to crisis management has studied a variety of dependencies, includ- ing board size and board gender diversity. The attention literature, pertaining to crises, focused on board size generally finds that a larger board size decreases risk taking, and thus generally increases the crisis preparedness (Gallud Cano et al., 2020; Huang and Wang, 2015; Nakano and Nguyen, 2012;

Pathan, 2009). The equivalent literature focused on board gender diversity is less uniform. Argu-

ments that board gender diversity improves firm performance during crises (Sun et al., 2015), due

to a more effective risk management strategy as a product of a larger amount of female directors

(Chen et al., 2016), are not uncommon. However, there are also studies arguing that this is a spurious

correlation, i.e. that board gender diversity has no impact on firm performance during crises (Engelen

et al., 2012). More general articles have also been written studying corporate attention of crises in

Greek companies after the Greek government-debt crisis (Vouzas and Nizamidou, 2018), and studies

discussing how stakeholder theory is superior to shareholder theory when it comes to proactive crisis

management (Alpaslan et al., 2009). However, it is clear that substantial board structure research

Building on this knowledge, this study aims to perform a content analysis on interim reports in order to explore whether there is a correlation between either the size or gender diversity of boards of directors at Swedish companies and the attention to the COVID-19 pandemic displayed by the top level management of respective company. This will contribute to current research by giving insight into the aforementioned relationship, as well as comparing the strength of the relationship to that of the control variables using regression analysis.

This report is structured as follows. In section 2, the underlying theory regarding board size and

gender diversity is presented, which the hypothesis development (section 3) summarizes and con-

cretizes. The content analysis methodology and regression model are discussed in section 4, as well

as results presented in section 5, and discussed in section 6. Concluding statements are presented in

section 7.

2 Theory

Within this section previous research regarding corporate attention board size, and board gender diversity is discussed, as well as its usefulness to this study.

2.1 Corporate attention

The expression “attention” is in itself quite broad. Within the world of corporate research the concept

“attention-based view” has come to be quite popular, which is a method of analysis that has proven

useful a number of times (Barnett, 2008). Attention is primarily, within this field, what someone or

something is focused on. Sullivan (2010) demonstrates quite concisely how an attention-based view

can be utilized to study corporations. She does this through examining primarily which property

decides what problem the attention of management teams is focused on, finding that urgency is a

dominant reason. A critical factor that may limit the generalization of her study is however that the

study is focused on a government agency. This finding is still another reason to study the attention

of top level management during COVID-19, as one can assume that pandemic-related problems hold

a substantial sense of urgency. Similarly, Greve (2008) discusses the matter of sequential attention

and in which order management prioritizes which problem in conjunction with what goals holds their

attention. There is also research (primarily based on quantitative data, which could be problematic

considering the following conclusions) suggesting that the attention companies pay to risk shifts with

company size and age (Wennberg et al., 2016), and that an inability to detect and remedy issues

within an organization, due to a disparity in attention, could be detrimental to a company (Rerup,

2009). Another shift in managerial attention is, however, found to be due to deregulation, which is

not in conflict with the theory that a shift due to size or age exists, but may instead be another real-

ization of the same effects (Cho and Hambrick, 2006). Other related research regarding the structures

of attention have considered how new issues are introduced into an organization’s already existing

hierarchy of problems with various levels of urgency (Joseph and Wilson, 2017), or how international

firm performance is correlated to top level executives time devoted to understanding the global mar-

ket (Bouquet et al., 2009). An issue in these latter studies does arise, as they are primarily focused

on either data gathered from a questionnaire, or a singular case study. Both articles discuss these issues, but the effect of a limited generalization of the results remain. It is also hypothesised that varying types of management control affects the way attention manifests itself in a company (Ocasio and Wohlgezogen, 2010). The collection of research presented here does, hence, imply that when such a considerable issue as COVID-19 appear, most companies attention should shift towards this issue.

There is, however, a few principal critiques that should be brought to light concerning prior research utilizing an attention-based view. A substantial fraction of previous research, including many of the aforementioned articles, neglects the impact of diverging interests withing their respective subjects of study, which in turn could skew both results and the hypothesised explanations for these, an effect that is hinted at in e.g. the article by Wennberg et al. (2016). Secondly, the absence of differentiation between the origin and the consequences of a shift in attention, which can have a similarly skewing influence of the results. An example of both aforementioned effects can be shown in the article by Rerup (2009) (Joseph and Wilson, 2017; Rerup and Salvato, 2012). Within this study the first of these critiques will not be taken into account, whilst the second is minimized naturally due to the nature of the methodology of the study.

2.2 Board size

There exists a large amount of research that uses board size as an independent variable. Within the

field the causal relationship between board size and firm performance is often studied (Dwivedi and

Jain, 2005), and a majority of studies used in this review find a robust negative relationship between

the board size and various economic indicators of firm performance. In Boone et al.’s (2007) “The

determinants of corporate board size and composition: An empirical analysis” the notion that an

increased board size leads to a trade off in firm-specific benefits (earlier proposed by Raheja (2005))

is rigorously supported. Similarly Bennedsen et al. (2008) show a negative correlation between board

size and firm performance in larger firms, but provide inconclusive results for small and medium size

firms. Eisenberg et al. (1998) do, however, provide conclusive evidence that this notion also holds

true for Finnish small and medium size firms. This is further supported for companies in Singapore

and Malaysia by Mak and Kusnadi (2005), for Thai banks by Pathan et al. (2007), and for companies in India by Garg (2007). Conyon and Peck (1998) also shows that board size in general is negatively correlated to firm performance for European companies. However, this negative correlation is not always the case as show by both Kyereboah-Coleman and Biekpe (2008) whom did not find a signif- icant correlation, and Mohamed (2009), whom examined a large number of banks and their Tobin’s Q ratio (a measure of assets value first proposed by Kaldor (1966)), and found a positive correlation between board size and firm performance.

However, a central concept many of the aforementioned articles, concerned with the relationship of board size and firm performance, neglects is the endogeneity within the subjects of their research.

A large number of these studies finds a concrete causal relationship, but fail to consider the possibil- ity of their data being endogenous, i.e. that there exists a possibility of the causality being fully or partially reversed, meaning that previous firm performance actually would affect board size (Wintoki et al., 2012). Interestingly, other research papers also claim that a large amount of previous research within the area rather is wrongly interpreted and, in fact, does not show this clear negative correlation.

Notable are Bennedsen et al.’s (2008) “The causal effect of board size in the performance of small and medium-sized firms”, and Garc´ıa-Ramos and D´ıaz’s (2020) “Board of directors structure and firm financial performance: A qualitative comparative analysis”, where the possibility of misinterpretation and various conflicting theoretical models are discussed.

It has also been hypothesised that this perceived negative correlation is due to the varying deci-

sive power of differently sized boards. Cheng (2008) finds that larger boards tend to make more

conservative decisions, and speculate as to whether or not this has a significant impact on board per-

formance. Huang and Wang (2015) do, however, find that smaller boards are more conservative when

it comes to decisions regarding debt financing. Within this field of study the concept of accountability

is central, often it is found that accountability decreases with board size, which one can assumes leads

to a lowered attention of crises (Omar et al., 2013). It needs to be noted that this assumption mainly

is founded in quantitative data, meaning that it is not founded on any interpretative component and

should be taken as such. All in all, the practice of using board size as an independent variable is widespread. This is positive as it can provide in-depth guidance on the subject if necessary.

2.3 Board gender diversity

The literature on gender diversity within boards as an independent variable is more heterogeneous than the literature examined in the section above, even if its effect on firm performance is a recurring subject. Within this subject area the impact of gender diversity on the probability of stock crash or bankruptcy is also popular. Adusei and Obeng (2019) examines whether bankruptcy becomes more or less probable, and finds that the latter holds true for boards showing greater gender diversity.

Qayyum et al. (2020) displays that gender diversity has a negative correlation to the stock price crash risk on the Asia-Pacific market, and Jebran et al. (2020) supports this further for specifically China. However, Shoham et al. (2020) shows that gender diversity has a negative impact on the phe- nomenon of company cross-listing. Goodstein et al. (1994) further shows that board diversity may be a significant constraint on strategic change in turbulent times, and Adusei (2019) shows that gender diversity may have a negative impact on “technical efficiency”. These latter notions are, however, disputed, e.g. Ahmadi et al. (2018), Merve and Cemil (2016), Ozdemir (2020), and Song et al. (2020) all show a positive correlation between gender diversity and firm performance, throughout the world.

Low et al. (2015) also supports this, but further brings to light the finding that these positive effects seem to be diminished in countries with higher female economic participation and empowerment.

They reason that this most likely is due to tokenism diminishing firm performance. Similarly, Liu

et al. (2014) support that gender diversity has a positive impact on firm performance, but only in so

called legal person-controlled firms, and not in state-controlled firms (in China). Li and Chen (2018)

show that the same is true if, and only if, the firm’s value does not surpass some critical value. A

study in the Netherlands and Denmark did not find a significant correlation between the two at all

(Marinova et al., 2016), and other studies found that gender diversity can be directly harmful by

impeding strategic change, and hence diminishing firm performance (Kouaib and Almulhim, 2019;

Triana et al., 2014). Despite the varying conclusions made in previous research, the practice of using gender diversity within boards as an independent variable is widespread.

Similarly to the research concerned with board size, the issue of endogeneity arises within this field

too (Wintoki et al., 2012). A large fraction of the literature within this field concerned with firm

performance and its relation to board gender diversity overlooks the impact earlier firm performance

has on the board of directors of a company. Although this issue is not as grave as when considering

board size, it can still skew eventual results substantially. Furthermore, the generalized results within

this field are few, as many studies are limited to e.g. certain geographical areas, which reasonably has

a large impact on the results due to a large variance in political motifs such as quotation legislation

(Kagzi and Guha, 2017). This notion is also supported by the heterogeneity in results between studies

(see (Adusei, 2019; Adusei and Obeng, 2019; Jebran et al., 2020; Qayyum et al., 2020; Shoham et al.,

2020)). Criticism regarding the rigidity and possible manipulation of financial data has also been

brought forward, suggesting that data based purely on financial records may not be entirely reliable

(Dezs˝ o and Ross, 2011), a consideration occasionally omitted. Despite aforementioned limitations

within earlier research, in-depth guidance on the subject can still be obtained from earlier research.

3 Hypothesis development

This section concretizes the purpose of this study, by summarizing the aforementioned literature and developing two hypotheses.

3.1 The impact of board size on corporate attention to crises

The literature pertaining to board size and attention to crises is, at times, somewhat conflicting. On the one hand smaller boards tend to make more conservative decisions when it comes to debt financing (Huang and Wang, 2015) and display a higher accountability (Omar et al., 2013), but on the other hand larger boards tend to, in general, make more conservative decisions (Cheng, 2008). When it comes to risk taking however, within the field of risk management and crisis attention, there exists a clear majority of articles advocating for the moderateness of larger boards (Gallud Cano et al., 2020;

Huang and Wang, 2015; Mary et al., 2014; Nakano and Nguyen, 2012). This uniformity in previous research leads to the first hypothesis of the study:

H1: An increased board size positively impacts the attention paid to the COVID-19 pandemic by top level management.

3.2 The impact of board gender diversity on corporate attention to crises

The subject of board gender diversity and its impact on risk management and crisis attention is

less homogeneous than it’s equivalent regarding board size. This research field displays articles on

both sides of the spectra. One side argues the point that board gender diversity significantly impedes

strategic change in turbulent times (Goodstein et al., 1994; Kouaib and Almulhim, 2019; Triana et al.,

2014). Whereas the other end of the spectra argues that the probability of bankruptcy and stock crash

significantly decreases with an increased board gender diversity (Adusei and Obeng, 2019; Jebran et

al., 2020; Qayyum et al., 2020). Between these two there also exist articles who found no correlation

of the two (Engelen et al., 2012; Marinova et al., 2016). Furthermore, the positive effects of board

gender diversity seem to be diminished in countries displaying higher female economic participation

and empowerment, which is the case in Sweden (Ortiz-Ospina, 2018), and is reasoned to be due to to- kenism (Low et al., 2015). However, all in all, there seems to exist a majority of research arguing that increased board gender diversity improves firm performance during crises (Sun et al., 2015), due to an improved crisis management (Chen et al., 2016). This produces the second hypothesis of this study:

H2: An increased board gender diversity positively impacts the attention paid to the COVID-19 pandemic by top level management.

3.3 Analytical model

Figure 1 shows the analytical model of the study, hypothesising that board size and gender diversity are positively correlated to the attention to COVID-19 the top level management of a company displays.

Board size

Gender diversity

Covid-19 Attention to

Figure 1: Analytical model of the problem, displaying independent variables (to the left) and their

impact on the dependent variable attention (to the right).

4 Method

This section begins by presenting, and advocating for, the general methodology used in this study, followed by the specifics of how the literature review was conducted and helpful material regarding the content analysis. The final part discusses explicitly how the data was gathered and processed.

4.1 General process

In order to study the produced hypotheses three components were needed. These are measures of the

board size, the board gender diversity, as well as the attention of COVID-19 of top level management,

for each company studied. The set of companies that was used in this study consisted of the compa-

nies of the OMXS Large Cap list. Specifically this list was chosen due to convenience and simplicity,

as these companies can be assumed to follow standard financial reporting procedures, be considered

as legitimate, and operate with a board structure more familiar to the author (compared to a typical,

say, American board structure). The first two components needed to study these companies, board

size and gender diversity, are the simpler ones. These have been manually collected for each company

by visiting their web pages and recording the total number of board members and the number of

female board members. The board gender diversity was then quantified as the fraction of female

board members divided by the total amount of board members. The board size was normalized by

dividing each individual company’s board size by the maximum board size found, in order to simplify

the calculations. The quantification of top level managements COVID-19 attention can be considered

to be the most vague measurement out of the three, in this study it will be considered to be the

frequency with which a company mentions a set of keywords in their interim reports, i.e. the number

of times a keyword is mentioned divided by the total amount of words written in each report. The

set of keywords used consists of “covid”, “corona”, “epidemic”, and “pandemic”, and all of the afore-

mentioned keywords various inflections. This can be considered to be one of the more basic variations

of content analysis, as it only analyses the reports on a word-by-word level, as opposed to a sentence

or paragraph level, further discussed in subsection 4.2. However, this form of content analysis is still

a useful tool and has been successfully used numerous times (Slattery, 2014). The reasoning behind

quantifying the attention to the COVID-19 pandemic of top level management as this aforementioned frequency arises from the field of sentiment analysis, where the frequency of keywords mentioned is interpreted as indicators of the author’s underlying opinions or beliefs. This form of analysis has become more popular with the rise of artificial intelligence and machine learning technology, and has been widely used to study corporate financial reports (H´ ajek and Olej, 2013; Pagliarussi et al., 2016;

Ren et al., 2013). The practicalities of recording the frequencies used in this study are discussed below, in subsection 4.3.

Furthermore, throughout this study the term “top level management” is used. This has the primary purpose of encompassing both the board of directors and senior management within the companies.

The necessity for a term such as “top level management” arises when one takes a closer look at the

purpose of this study, which is to examine the attention to COVID-19 of top level management,

based on the frequency of mentioned keywords in the interim reports of respective company, and

see how this varies depending on the board of directors of respective company. Within this purpose

there seems to be a logical fallacy arising, as the board of directors do not actively have a hand in

producing the interim reports. There are, however, reasons to question this fallacy. It is true that

the board of directors do not have a hand in the day-to-day, and are not immediately responsible for

the interim reports of a company, but they are responsible for choosing a CEO and are ultimately

responsible for his or her decisions. This creates an indirect coupling between the board of directors

and the interim reports of each company, through the CEO and senior management. It is also true

that the board of directors should have a more long-term plan in sight, as opposed to the CEO’s more

direct operational planning and decision making. This has been, and continues to be, the way that

the board of directors interacts with senior management. During crises the boards of directors field of

practice does, however, expand. It is still the CEO’s responsibility to handle the day-to-day, but the

board needs to be one of action, in order for the company to perform well. Boards have an unique

possibility to engage in a supportive role to senior management, which is more crucial than ever

during this pandemic. Allowing the senior managers to focus on immediate threats and opportunities

with the confidence that a competent set of board members, focused on immediate fundamentals and

dealing with secondary threats, can keep the company aimed at its long-term goals (Deloitte, 2020;

von Post and Pozen, 2020). This may be an unorthodox practice, but, as put by Konigsburg et al.

(2019):

In a crisis, when the stakes are high and scrutiny is intense, the board has a unique role.

Stepping in may be uncomfortable, but stepping aside is not an option.

Perhaps a more direct research question would have been focused on the structure of the senior management team, as opposed to the board structure, but considering that the scope of this study is not wide enough to encompass both, and this study has a focus on corporate governance, the decision to focus on the board of directors was made.

4.2 Literature review and Content analysis

To structure the literature review a few limitations were set. Due to the large sample size of pre- vious research, it was determined that studies conducted within the last 30 years were to be used.

To review the use of board size as an independent variable the set of selected keywords chosen were

“board”, “size” and “corporate”. To study earlier literature pertaining to board gender diversity as independent variable the keywords “board”, “gender”, “diversity”, and “corporate” were used. The keywords “crisis”, “attention”, “management”, “corporate”, and “risk” were used to conduct the lit- erature review of corporate attention. The review of previous content analysis literature is not limited to the last 30 years, due to the smaller sample size. The set of chosen keywords were here “content”,

“analysis”, “annual”, “quarterly”, “interim”, “financial”, and “report”.

By studying the literature relating to previous content analyses, and how these are used to determine

the dependent variable, a deeper understanding of the methodology can be obtained. An example of

content analysis applied to annual reports, similar to the methodology of this study, is the study “The

Importance of Information Technology: An Empirical and Longitudinal Study of the Annual Reports

of the 50 Largest Companies in the United States” by Peslak (2005). In this study content analysis is

used to find information technology keywords, and the author finds that there exists a decline in the

use of these terms. Lajili and Z´ eghal (2005) uses the same methodology to examine risk information disclosures in Canadian TSE 300 annual reports, their findings show a high degree of risk disclosure intensity, though the authors still suggest a more formalized procedure for risk disclosure. The study

“Content Analysis of Annual Reports for Corporate Strategy and Risk” display the inherent power in digitalised content analysis, while examining corporate strategy’s influence on risk and return in three industries (Bowman, 1984). Corporate responsibility was the main concern of a report written by Everaert et al. (2007), which also utilised content analysis of annual reports. The four aforementioned reports all provide examples of rigorous content analysis of annual reports, which are assumed to be closely enough related to interim reports for the examples to hold their validity. Other studies have used content analysis to examine other forms of media, Furrer et al. (2008) examines management studies, Soyoen and Jisu (2010) look at corporations online blogs, and Landrum and Ohsowski (2018) study corporate sustainability reports. In combination with previously mentioned studies these ex- pand the exemplary content analyses and will assist in the content analysis of this study.

A point of criticism that content analysis faces, especially at a word-by-word level, is the issue of

attempting to infer meaning from an inherently abstract set of data. The aforementioned effect is

further amplified by the inherent reductive properties of this form of content analysis and, hence, is a

central criticism that needs to be taken into account when analyzing the results of this study (Mukher-

jee and Bhattacharyya, 2013). However, this study does avoid another set of issues often associated

with modern content analysis, issues arising from the use of convenience sampling. As technology has

advanced and data is readily available in modern times, the automatic sampling of said data, within

expansive content analysis studies, is often based on some common feature. This feature could be

e.g. a common hashtag defining a set of tweets or a common reference defining a set of articles. The

primary issue with this method of data collection is that it can be nearly impossible do determine

whether the resulting set of data is representative of the research question. As this study rather uti-

lizes a purposive sampling (one based on some logic pertaining to the research question (Riffe et al.,

2019), i.e. a set of interim reports directly connected to a well defined set of companies), the study

avoids these issues. But instead, it runs into another issue, the matter of limited generalizability due

to a set of data that is not representative of a larger group (Lacy et al., 2015). At times some or all of these points of criticism are skimmed over when content analysis is used as a primary methodology of data collection (Everaert et al., 2007; Furrer et al., 2008; Pagliarussi et al., 2016), which is something this study attempts to keep in mind, if not avoid entirely.

4.3 Implementation and Statistical Model

In order to process the larger amount of data that was used in this study a more quantitative method- ology was used. Apart from being able to easily process larger amounts of data, this way of working can also be seen as advantageous as it removes the possible element of human bias (Basias and Pollalis, 2018). To determine the frequencies corresponding to the COVID-19 attention of companies Python (van Rossum, 1995) was used. The necessity to create a new program able to accomplish this arises as the underlying nuances of the interim reports are large, and as there is a substantial number of them to process, two factors many existing text readers are not able to deal with simultaneously at an adequate pace. The script written, seen in Listing 1, accomplishes this and was used in part to, for each company studied, count the total number of words in an arbitrary interim report, determine how many times a keyword was mentioned in said report, and sort the resulting values based on company and financial reporting period. This is the purpose of the first method, word count().

It is not uncommon to see the variable firm size being used in the field of empirical corporate fi-

nance, which is not surprising as it can be seen as a fundamental characteristic of a firm. This

practice is typical in part due to the “measurement effect” in “size effect” (Dang et al., 2017). In

order to account for this and see if the size of a company significantly affected the results the chosen

control variable was chosen as the number of employees. A considerable con of using the number of

employees as a control variable is that it does not take part time workers in consideration, which can

skew the results. However, as previous research has concluded that number of employees is one of

the preferred measures of firm size when studying corporate governance (Hashmi et al., 2020), and

that it is an easily accessible statistic, it was determined to be used as a control variable. The data

was gathered partially by utilizing web scraping, but needed to be complemented manually due to

a lacking data set. In the method control scrape() (Listing 1) the automatic part of this process can be seen, which gathers data from TradingView, chosen due to its easy access to a quite large set of data. The use of sites such as “TradingView” is not common practice in previous research on corporate governance, but as it was deemed to be a legitimate site with an easily accessible database, it was used to gather most of the data to the control variable. Manually complemented data was gathered from the site of respective company.

The method var gathering() (Listing 1) was written purely to simplify the data transfer from Python

to Matlab (MATLAB (R2020a), 2020), which was used to perform the statistical calculations and

plotting (see Listing 2). As is customary when performing correlational research the modeling was

done by first checking the data sets for heteroscedasticity (Basias and Pollalis, 2018), in order to de-

termine which statistical model was appropriate for the data sets. Whether or not heteroscedasticity

existed in either data set was determined using Engle’s ARCH test (Engle, 1982). Furthermore, the

variables need to be checked for multicollinearity, this was done using Belsleys collinearity diagnostics

and seeing what the resulting condition indices were. If they were greater than 10, extra care needed

to be taken when performing the regression (Belsley, 1991). As heterosckedascity was not present

and the multicollinearity of the variables was low (lower than seven for all variables), the method

used for the linear regression was linear least squares (Carroll, 1982). However, if heteroscedasticity

would have been present or the data sets had displayed considerable multicollinearity a more robust

regression model would have had to be used, in this case either general or robust least squares would

have been adequate. (Johnston and DiNardo, 1997)

5 Results and Analysis

The results take the form of plots of the collected data and accompanying regression, with tables of corresponding values and errors. This section will begin by presenting the single variate regres- sion analysis only considering board size or gender diversity, followed by an analysis considering the combination of the two variables. After these the control variable, firm size measured as the natural logarithm of the number of employees of each company, is introduced to the analysis. At first by considering its impact on board size and gender diversity separately, and concluding by considering its impact on the two variables combined.

The four data sets that were gathered, and some of their properties, are displayed in Table 1. Here we can see that the independent variable, attention, displays a mean value of 0.2741%, and a com- paratively low variance. The set describing board size does however show a large variance, where the minimum board size found was 2, and the maximum 17. Similarly, board gender diversity also shows a large variance (258.68%), with a mean of 32.740%. This indicates that neither of these data sets can be approximated using a normal distribution (or at least is very badly approximated for a 100 data points). The control variable does, however, display a mean of 8.3503 and a variance of 4.7845.

Table 1: Properties of the used data sets

Variable Mean Median Variance Range

Attention [%] 0.2741 0.2472 0.0260 (0, 0.63)

Size [#] 8.7800 8 13.305 (2, 17)

Gender Diversity [%] 32.740 33.333 258.68 (0, 66.67)

Employees [ln(#)] 8.3503 8.8467 4.7845 (2.71, 12.82)

5.1 Analysis omitting the control variable

The focus of Figure 2 is to display how the attention of top level management is related to board size. Here each blue dot represents a company listed on Nasdaq OMX Stockholm Large Cap and its recorded attention to the COVID-19 pandemic (ranging from 0 to 1) and board size (ranging from 2 to 17). A linear fit to this data is represented by a red line, whose specifics are described in Table 2. Figure 2 displays the lack of a significant correlation between board size and the attention to COVID-19 of top level management. There may seem to be a slight increase in attention with an increase in board size, but by studying the errors of the fit (in Table 2) one can see that there is a large residual sum of squares (RSS = 3.917) and a coefficient of determination close to zero (R

≈ 0.01), implicating that this correlation does not exist, and indicating a future rejection of H1 .

2 5.8 9.5 13.3 17

Size (#) 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Attention

Fit of size

data fitted curve

Figure 2: The data where the attention to the COVID-19 pandemic of the top level managements of a company is plotted over board size (blue dots), and the resulting fit only considering board size (red line).

Figure 3 displays how the attention of top level management is related to board gender diversity. Here

each blue dot represents a company listed on Nasdaq OMX Stockholm Large Cap and its recorded

attention to the COVID-19 pandemic (ranging from 0 to 1) and board gender diversity (ranging from

0% to 67%). A linear fit to this data is represented by a red line, whose specifics are described in Table 2. Similarly to Figure 2, Figure 3 does not show the existence of a correlation between the attention to COVID-19 of top level management and board gender diversity. The specifics of the fit, seen in Table 2 further supports this notion of a lack of a correlation by displaying a R

< 0.01 and a RSS = 3.9541, indicating both a non-existent correlation and a large deviance from the fit, suggesting a future rejection of H2 .

0 16.7 33.3 50 66.7

Gender Diversity (%) 0

0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Attention

Fit of gender diversity

data fitted curve

Figure 3: The data where the attention to the COVID-19 pandemic of the top level managements of

a company is plotted over board gender diversity (blue dots), and the resulting fit only considering

board gender diversity (red line).

Table 2: Data regarding fits of board size and gender diversity respectively, without control.

Fits of respectively size and g.d. Normalized coefficients, with 95% CI

Size 0.11375, (-0.072089,0.29959)

Gender Diversity 0.061607, (-0.10445,0.22767)

Errors for fit Size Gender Diversity Residual sum of squares (RSS) 3.917 3.9541

R

0.014832 0.0055004

Root mean square error (RMS) 0.19992 0.20087

From the initial plots seen in Figures 2 and 3 it is quite clear that there exist no statistically significant

correlation between neither board size nor board gender diversity and the attention to the COVID-

19 pandemic of top level management of the companies listed on Nasdaq OMX Stockholm Large

Cap. Figure 4 combine these two, in order to ensure that no combination of the two independent

variables produce a different outcome. The blue dots in Figure 4 each represents a company, and

the plane represents the resulting fit of both independent variables, where higher predicted values of

COVID-19 attention of top level management are represented by a lighter color, and lower values by

a darker color. Table 3 displays the specific coefficients and errors corresponding to the fit in Figure

4. Unsurprisingly, analysing this fit shows that there is no statistically significant correlation present,

as the residual sum of squares is large (RSS = 3.905), and the coefficient of determination is close to

zero (R

≈ 0.02).

Figure 4: Resulting data considering both independent variables, without control variable

Table 3: Data regarding plane fit

Fit for size and g.d. Normalized coefficients, with 95% CI Size 0.10521, (-0.08389,0.29431)

Gender Diversity 0.046238, (-0.12194,0.21442)

Errors for fit of size and g.d.

Residual sum of squares (RSS) 3.905

R

0.017847

Root mean square error (RMS) 0.20064

5.2 Analysis considering the control variable

In order to check the magnitude of the impact of the hypothesised independent variables a control variable was introduced. This control variable was determined to be firm size measured as the natural logarithm of the number of employees of each company. Figure 5 shows the effect the introduction of this control variable has on the fit when omitting the second independent variable, board gender diversity. The specific coefficients and errors of this fit can be seen in Table 4. The lack of a correlation between board size and COVID-19 attention of top level management remains utterly clear, with the confidence interval shifting even closer to zero than before (compare Tables 4 and 3). Hence, it is safe to certainly reject H1 for companies listed on Nasdaq OMX Stockholm Large Cap. Furthermore, the control variable, firm size, is the dominant one of the two as it displays an impact many times greater than that of board size.

Figure 5: Resulting data and fit of board size, with control.

Table 4: Data regarding fit of board size, with control

Fit of size, with control Normalized coefficients, with 95% CI

Size -0.025229, (-0.22248,0.17202)

Employees 0.40174, (0.15367,0.64981)

Errors for fit of size, with control Residual sum of squares (RSS) 3.54

R

0.10966

Root mean square error (RMS) 0.19104

Figure 6 is the analogue to Figure 5, but considering only the independent variable board gender diversity and the control variable firm size, measured as the natural logarithm of the number of employees of a company. Each blue dot represents a company and the plane the predictive fit of these aforementioned variables to the COVID-19 attention of top level management. Table 5 displays the characteristics of this fit. Figure 6 further entombs the idea of a correlation between board gender diversity and COVID-19 attention of top level management of companies. Hence, it is safe to certainly reject H2 for companies listed on Nasdaq OMX Stockholm Large Cap. One can, however, identify the reverse effect of the control variable on this independent variable board gender diversity, where, as opposed to its impact on the independent variable board size, it enhances the magnitude of the independent variables impact in this fit (see Table 5). This effect could be a hint to a possible connection between the board gender diversity of OMXS Large Cap companies and the size of these companies (measured as the number of employees), but is not noticeable enough to infer anything.

Once again the impact of the control variable is significantly larger than that of the independent

variable.

Figure 6: Resulting data and fit created from board gender diversity, with control.

Table 5: Data regarding fit of board gender diversity, with control

Fit of g.d., with control Normalized coefficients, with 95% CI Gender Diversity 0.072025, (-0.085423,0.22947)

Employees 0.39177, (0.16912,0.61442)

Errors for fit of g.d., with control

Residual sum of squares (RSS) 3.5125

R

0.11657

Root mean square error (RMS) 0.19029

Table 6 displays the coefficients of a multiple linear regression considering the impact of both inde-

pendent variables and the control variable on the COVID-19 attention of top level management of

companies listed on Nasdaq OMX Stockholm Large Cap. As it is to little use visualising data in four

dimensions corresponding figures were omitted. The coefficients of the resulting fit, combined with

the coefficient of determination conclude that there indeed is no correlation between neither board

size nor gender diversity and COVID-19 attention, further repudiating both H1 and H2 .

Table 6: Resulting coefficients and R

when creating a fit to all independent variables, with control.

Fit of all variables, with R

Size -0.045088

Gender Diversity 0.079277

Employees 0.41682

R

0.11839

Interestingly, throughout the use of the control variable, firm size measured as the natural logarithm

of the number of employees of each company, it is clear that, compared to the independent variables,

it has a larger impact on the COVID-19 attention of top level management of each company (see

Tables 4, 5, and 6). This could give one the idea that firm size might be related to attention in some

way. However, looking at errors of the corresponding fits suggest that this most likely is not true, and

if it were to be true, there would need to be at least one mediating variable in the aforementioned

connection, as the current model does not show this connection clearly.

6 Discussion

All in all, it is clear that neither H1 nor H2 holds true, which is interesting as it repudiates a few conclusions one could draw from previous research. There are a number of factors that plausibly could play a role in this overwhelmingly clear rejection of both hypotheses, the ones that will be discussed here are, in order, the possibility of a faulty conclusion that the board of directors would be more directly involved during a crisis, the possibility of a content analysis too basal to adequately draw any conclusions, and more general inaccuracies in the method of this study.

The question of too quickly assuming that the board of directors would be more directly involved during crises, hence affecting something as day-to-day as an interim report, has been one of impor- tance during this study. It is a quite common stance that the board of directors remain focused on big-picture planning and stay out of the day-to-day operations, which would include the writing of interim reports, as the CEO (and perhaps the CFO) are primarily responsible for these. There is, however, literature advocating for the phenomenon of increased board activity during crises (Deloitte, 2020; Konigsburg et al., 2019; von Post and Pozen, 2020), which, if this previous research is to be taken at face value as truthful and legitimate, indicates either the need for a revised method of con- ducting a study such as the one conducted here or a deeper and more precise definition of in what way boards are more active during crises. The latter of which raising the question of the activity of the board of directors during crises, and that it needs to be investigated further in the future. The former of these two lead to the question of the validity of the methodology of this study.

The analysis in this study is based on one of the more rudimentary forms of content analysis, se-

mantic analysis at a word-by-word level. The question of comparing the COVID-19 attention of top

level management of different companies based on the frequency of mentioned keywords has been a

reoccurring one, as it intuitively may not be clear why this frequency would be a good measurement

of the attention of the authors of the interim reports (top level management). Studies based on a sim-

ilar reasoning are, however, widely accepted and legitimate, as this form of content analysis is being

ble change to the methodology could have been, instead of focusing on a rudimentary content analysis for a larger number of companies, focusing the study on a select few companies and conducting a more advanced content analysis. This other form of content analysis could have been a combination of the current one used, at a word-by-word level, complemented by manual interpretations of both sentences and paragraphs of the studied interim reports. This would be a more qualitative methodology, which possibly could have yielded a richer basis of data to analyze, compared to the current, quite purely quantitative, methodology. This is no guarantee that the results would have been any different, but it would have given a greater confidence in the fact that no nuances of the language in the interim reports were overlooked, which is a very real possibility when conducting a content analysis at a word-by-word level.

Other possible inaccuracies in this study could have been, among other things, due to the human factor. A substantial amount of data was gathered manually, and the parts that were automated were done so by the author (see Listing 1), meaning that a certain margin of error is to be expected.

By checking the manually gathered data multiple times this margin was minimized for the manually

gathered data, and by implementing safeguards against unreasonable values and checking the validity

of selections of the automatically gathered data this margin was minimized for the automatically

gathered data. Furthermore, systematic errors in the softwares used could exist, skewing the results

of the study. This is, however, quite unlikely as all software utilized in this study is widely used and

the risk of encountering such errors in well established parts of the software is low.

7 Summary

This study was conducted in order to explore whether or not the attention to the COVID-19 pandemic of top level management of companies listed on Nasdaq OMX Stockholm Large Cap was correlated to the board structure of respective company. By using board size and board gender diversity as measures of board structure, and utilizing content analysis to obtain a comparative measure of the attention of each company, their relation to each other was studied. Listing 1 displays the script written, in Python, created to perform the content analysis itself. The subject of the content analysis were the three published interim reports from 2020 of each company studied, resulting in around 300 total interim reports for around 100 companies. The resulting sets of data was then analyzed using Matlab (see Listing 2), where linear fits were constructed to various combinations of the variables used. Neither data set displayed heteroscedasticity, nor high levels of multicollinearity, indicating that the regression model linear least squares was adequate, and, hence, was used.

7.1 Conclusion

Both hypotheses developed in the preamble of this study were rejected. The rejection of both H1

and H2 were derived from the fact that both board size and board gender diversity clearly displayed

an absence of correlation to the attention to COVID-19 of top level management. This conclusion

was drawn for multiple reasons. When one inspects the error measurements of the various fits, all of

them display root mean square errors close to 0.2, and residual sum of squares larger than 3.5, which,

considering that the value of “attention” ranged from 0 to 1, is considered large, indicating that no

fits were good. Furthermore, the coefficient of determination was very close to zero (R

< 0.02) when

considering only the independent variables, and when also considering the control variable, firm size,

it only rose to about 0.1. The control variable also displayed a much larger impact on attention than

either of the independent variables, consistently displaying an impact 5 – 10 times larger than either

independent variable.

7.2 Sources of Error and Future Work

Despite the non-existent correlation of attention to COVID-19 of top level management and either board size or board gender diversity, this study does bring some clarity and a new question to the research field of management attention. As the firm size of a company seems to hold some significance over attention further research could be conducted investigating this connection. It is possible that it only is a spurious relationship, but the significantly larger impact on attention (compared to the independent variables) does indicate that there could exist an underlying relationship between firm size and attention to COVID-19 of top level management. This relationship may be one of a mediating nature, as indicated by the poor quality of direct fits in this study. Furthermore, if the methodology of this study were to be reused, it would be interesting to see the effect of a more comprehensive content analysis, not only containing a word-by-word analysis, but also an interpretative component focused on entire paragraphs. Exploring whether or not the results would differ between industries could also be very interesting, as there is a large variation in what way these are affected by the pandemic.

Going forward there should be numerous possibilities of extending our current knowledge regarding

both attention to crises by management, and general risk management in companies. Hopefully this

study can, to some extent, aid in these future efforts.

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