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Master Degree Project in Innovation and Industrial Management

FORESEEING CORPORATE FORESIGHT

- A study of corporate foresight and future orientation in annual reports

Jessica Malmström & Matilda Källman

Supervisor: Daniel Ljungberg

Graduate School

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Abstract

Corporate foresight is the techniques, practises and processes of how firms detect, interpret and respond to change in their environment. Since corporate foresight benefit firms, this is something which they should want to communicate to share- and stakeholders. Based on this, the purpose of this study is to determine which industries communicate corporate foresight and if it is related to their performance. To determine this, two different analyses were performed. Firstly, a content analysis of corporate foresight keywords in the CEO letters of the annual reports was made. The amount of keywords was put in relation to a literature based ranking which was made for the investigated industries. The empirical results resembled the literature based ranking, implying that an industry’s expected level of corporate foresight is reflected in their CEO letter. Secondly, the amount of corporate foresight keywords collected in the content analysis were used in a regression analysis between corporate foresight keywords and the performance of the industries. The aim being to determine if a firm’s performance, in terms of growth, performance, innovation and flexibility, affected the amount of corporate foresight keywords. A relation could be detected in all industries but it varied in strength and in positive/negative values in the investigated industries. This implies that the external communication of corporate foresight is, to a varying degree, affected by a firm’s performance and that this is visible in the CEO letter of an annual report.

Keywords: Corporate Foresight, Future, Communication, CEO letter, Annual Report

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Introductory Remarks

Firstly, we would like to thank our supervisor, Daniel Ljungberg, for taking the time to give us feedback and answer any queries that occurred during the thesis process. We would also like to thank our friends, families and classmates who have been supporting and understanding throughout the thesis, making sure that we could put our focus where most needed. Lastly, we would like to thank each other for putting up with our respective nonsense and with a lot of persistence and hard work delivering an extraordinary master thesis.

“(Key)words are, in my not-so-humble opinion, our most inexhaustible source of magic.”

Professor Dumbledore

(Harry Potter and the Deathly Hallows: Part 2, 2011)

Gothenburg, June 2017

Matilda Källman & Jessica Malmström

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Table of Content

1 Introduction 1

1.1 Problem background 2

1.2 Research question 3

1.3 Delimitations 4

2 Literature 6

2.1 Corporate Foresight 6

2.1.1 Future Management 8

2.1.2 Value from corporate foresight 9

2.2 Working with corporate foresight 11

2.2.1 Leadership 14

2.3 Levels of corporate foresight 15

3 Methodology 17

3.1 Research Approach 17

3.1.1 Company and Industry Selection 17

3.1.2 Company Sample 19

3.2 Content Analysis 20

3.2.1 Keywords 21

3.2.2 Data Assembling of Keywords 23

3.3 Regression Analysis 25

3.3.1 Financial Variables 26

3.3.2 Conducting the Regression Analysis 27

3.4 Theoretical Framework Assembly 28

3.4.1 Inclusion and Exclusion Criteria 29

3.4.2 Literature Credibility 29

3.5 Research Quality 29

4 Descriptive Results 31

4.1 Results from content analysis 31

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4.2 Results from regression analysis 34

5 Analysis 37

5.1 Content analysis 37

5.1.1 Mean Industry Ranking 38

5.1.2 Median Industry Ranking 38

5.1.3 Industry Ranking excluding “Growth” 39

5.2 Regression Analysis 41

5.2.1 Financial Variables 44

5.2.1.1 Number of Employees 45

5.2.1.2 EBIT 45

5.2.1.3 R&D Intensity 46

5.2.1.4 Current Ratio 47

6 Conclusion 49

6.1 RQ1 - Which industries focus more on expressing their future orientation

externally through the help of their CEO letters from their annual reports? 49 6.2 RQ2 - Is there any relation between the amount of corporate foresight keywords

and the performance of the firm? 50

6.3 Future Research 51

References 53

Appendices 56

Appendix 1: Oil & Gas, Materials and Utilities Company List 56

Appendix 2: Financials Company List 57

Appendix 3: Consumer Industry Company List 58

Appendix 4: Healthcare Company List 59

Appendix 5: Technology & Telecom Company List 60

Appendix 6: Industrials Company List 61

Appendix 7: Words used in the systematic review 62

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List of Figures

Figure 1: Literature structure 6

Figure 2: Value created by corporate foresight 10

Figure 3: Levels of innovation 16, 37

List of Tables

Table 1: Keywords, Connecting keywords and Motivations 22

Table 2: Keywords for data collection 24

Table 3: Keyword data for OGMU, Financials and Consumer Industry 31 Table 4: Keyword data for Industrials, Technology & Telecom and Healthcare 33 Table 5: Descriptive statistics for All Industries 35 Table 6: Descriptive statistics for each industry separately 35 Table 7: Correlation table for the different variables 36 Table 8: Mean and Top 5 words in the different industries 38 Table 9: Median and Top 5 words in the different industries 39 Table 10: Mean and Median for the different industries excluding growth 40 Table 11: Summary of the regression results from an industry perspective 43 Table 12: Regression between the percentage of corporate foresight keywords

and financial measurements 45

List of Abbreviations

CEO Chief Executive Officer CF Corporate Foresight

EBIT Earnings Before Interest & Tax

EU European Union

OGMU Oil & Gas, Materials and Utilities R&D Research and Development

UK United Kingdom

US United States

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1 Introduction

The ability to analyse and prepare for the future is something that all great leaders, not only within corporations, have had to face. In everything from preparing for battles to making political decisions this has and will be a challenge. One famous example of this is when the UK’s Prime Minister Harold Macmillan was asked which part of his job he found to be the most challenging. His response was “Events, my dear boy, events.”

(Burke, 2014). This demonstrates the difficulty of unknown events that even a Prime Minister faces.

According to Kotler and Caslione (2010), chaos is the new normal state in the global economy. The chaos and fast pace of change leads to complexity and volatility in the business environment. Industries and markets emerge, evolve, collide, split and decline;

making it crucial for firms to cope with the uncertainties by adapting to the changing business environment (Vecchiato, 2015). What a company offers or produces will no longer be as important as its ability to handle the turbulent environment and risk and uncertainty that follows (Kotler and Caslione, 2010). Ratcliffe (2006) discusses how this change requires an entirely new mindset, one that can adapt easily to the uncertainties, changes and a more complex environment.

Corporate Foresight can be defined as the activities, techniques, processes and other means aiming at helping firms’ future growth and success by discovering uncertainties and change in the business environment. This also includes identifying the implications and being able to respond to change (Vecchiato, 2015). Rohrbeck and Gemünden (2011) agree that the purpose of corporate foresight is not to perfectly predict the future but to prepare for it by creating possible and probable options. The process of creating these options can help to discover and map events and uncertainties that are likely to affect the business environment in the future.

As these event and origin vary substantially, corporate foresight is something that many companies struggle with. Some of the problems that Becker (2002) noted were:

inaccurate results; a lack of communication; not utilizing the know-how of the people

working with foresight; as well as not implementing foresight activities in the company

internally. Sapriel (2003) also states that in general it is very hard to work with

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corporate foresight as it has become more complex due to a constantly changing global business environment. As Becker (2002) mentions, it is very important to integrate corporate foresight with the overall strategy of the company to deal with unknown situations most efficiently.

Corporate foresight is challenging but also, according to Vecchiato (2015), a prerequisite for growth and success of firms. Therefore, a firm who successfully engages in corporate foresight activities should want to communicate this to its stakeholders.

The argument is supported by findings in corporate foresight literature which state that corporate foresight activities generate value and benefits to an organization. One of these benefits, mentioned by Rohrbeck (2011), include that a company's external image can be enhanced and promoted from corporate foresight, since it shows that a company is future oriented and prepared for the future.

A prominent way of communication for firms is through their annual reports. According to Jack, Davison and Craig (2013) accounting involves communication as much as it involves measurement. For companies both internal and external communication is a requisite for success. The goal of annual reports is for companies to share information and meaning to its stakeholders, such as investors, customers and employees. A model about integrated reporting, developed by PwC, aims at supporting transparent and relevant accounting reporting. To achieve this goal, an accounting report should give a holistic and integrated picture of a company. This includes the company’s strategic picture of how it creates and captures value during the present but also in the future (Jack et al. 2013). A CEO has the possibility to communicate this in the CEO letter, which is usually among the first pages of an annual report.

1.1 Problem background

Since the benefits of corporate foresight should want to be communicated to

stakeholders, as mentioned by Jack et al. (2013) and Rohrbeck (2011), the annual

report is assumed to contain information and indications about a firm’s work with

corporate foresight and thereby their future orientation. Meaning that a firm which is to

some extent engaged in corporate foresight activities should communicate this in their

annual report. One argument against this conclusion is that an annual report should be

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informative and exhaustive without being too long. Nowadays, people want more information in a shorter content (Jack et al. 2013). When priorities are being made in the making of an annual report corporate foresight might therefore not be included. As industries have different characteristics, in terms of growth, innovation and flexibility among other things, it is possible to draw the conclusion that they do not work with and communicate corporate foresight in the same way. Resulting in varied levels of future orientation. It is therefore interesting to see to what extent the communication of future orientation varies between different industries. This also makes it interesting to investigate if there is a relationship between an industry’s performance and what the company communicates in terms of corporate foresight. This culminates in the following research questions for this thesis.

1.2 Research question

The purpose of this thesis is to investigate the relationship between the annual reports and corporate foresight, in order to see if corporate foresight and future orientation can be assessed through an annual report. More specifically to determine if the CEO letter from an annual report gives a representative image of an industry’s assumed level of future orientation and if this is related to a company’s performance. This leads to the following two research questions which will be investigated and answered in this thesis.

Research Question 1:

- Which industries focus more on expressing their future orientation externally through the help of their CEO letters from their annual reports?

Research Question 2:

- Is there any relation between the amount of corporate foresight keywords and the performance of the firm?

By answering these research questions, the hope of this thesis is to be able to determine

if there is a relationship between the expressed corporate foresight in the CEO letter in

the annual reports and the assumed level of corporate foresight. But also, to determine

the relationship between corporate foresight in the CEO letter and the performance of

the industries in question.

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1.3 Delimitations

The sample of companies used in this thesis was assembled with the help of the Nasdaq Nordic large cap listing. This was done to create a financial delimitation to make it easier to compare companies without having too large variations in their turnover, as the large cap companies have specific financial requirements. To only look at large companies is something that Daheim and Uerz (2008) chose to do in their study. They made this decision since it allowed the companies to have large resources but also because the majority of these companies had a lot of experience from working with corporate foresight. Appiah and Sarpong (2015) discusses that corporate foresight is something which have high costs. This was also a further motivation for selecting larger companies in this study as smaller might not make corporate foresight a priority, due to financial reasons. The industry categories from the Nasdaq Nordic large cap were also used in this study. The decision to investigate different industries was made as this would allow to group large amounts of data together for an easier comparison and for the possibility to detect industry related trends.

A second delimitation was to use solely Nordic companies, this in order to remove and/or minimize any cultural and structural differences in the annual reports in the ways they express themselves towards their stake- and shareholders. With the help of the Nasdaq Nordic, six industries were assembled with ten companies in each industry.

For a company to be used in the sample it had to be possible to access the company's

annual reports, more specifically the CEO letters in English for the years 2011-2015. If

this was not possible, the company was removed from the sample and randomly

replaced with another company from the same industry. This choice was made in order

to maintain high levels of data and to allow for fair assessments when comparing the

different industries. To allow for an equal comparison between the different industries,

which are further motivated and discussed in 3.1.1 Company and Industry Selection, a

restriction regarding the number of companies was set to 10. This limitation was set as

it would otherwise have resulted in more companies in certain industries and fewer in

other industries, making the comparison uneven. Another limitation set regarding the

industries was that in the ones where there were not enough companies, these

industries were merged with similar industries. This is motivated and further discussed

in 3.1.1 Company and Industry Selection.

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The decision to focus only on the CEO letter in the annual report was made since this part mainly consists of text, not emphasising numbers. This in order to perform the content analysis without collecting keywords from financial tables, where the words might have lost their meaning. Further, there are different varieties of annual reports and what they include, but most companies have a CEO letter in the beginning which summarizes the past year and talks about where the company is headed for the future.

These above mentioned aspects were the basis of limiting the content analysis to the

CEO letter.

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2 Literature

The literature review will begin by focusing on the general aspects and definitions of corporate foresight. This will be followed by the aspects of working with and leading corporate foresight, both in terms of the CEO and different departments, and opportunities and challenges that follow. This assembled literature was used in order to help explain corporate foresight whilst also motivating the corporate foresight keywords used in the content analysis.

Figure 1: Literature structure

2.1 Corporate Foresight

Corporate foresight is initially derived from the term strategic foresight. However,

corporate foresight emphasizes foresight in the private sector as opposed to strategic

foresight which is broader also including the public sector. Since the focus in this thesis

is on privately owned firms the term corporate foresight is best suited and therefore

used hereafter. Further, two different definitions of corporate foresights are mainly

used in research, foresight as a process and foresight as an ability of the firm. In this

thesis, corporate foresight is defined as an ability, rather than a process, since ability

involves processes and other means for which firms detect, interpret and act upon

uncertainties and change in the business environment (Rohrbeck, 2011).

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In the introduction, a definition of corporate foresight by Vecchiato (2015) was introduced. In this article, Vecchiato uses the word strategic foresight when talking about corporate foresight and provides this definition:

“... we define strategic foresight as the set of techniques, practices and processes that organizations use for:

detecting new events and changes in their external environment; exploring their likely evolution and effects;

and defining response options.”

/Vecchiato (2015) p.26

To get a broader spectrum, a second definition looked upon was that of Rohrbeck (2011) who defines corporate foresight as the following:

“... the ability to detect, interpret and respond to discontinuous change. This capability will be referred to as corporate foresight.”

/Rohrbeck (2011) p.1

The reason for using these two authors and their definitions is that they are frequently mentioned in the literature by other authors, but also since they have themselves, actively published several publications related to corporate foresight earning recognition in this field.

Rohrbeck (2011) also discusses how research in corporate foresight has a cross- functional perspective from three main areas; innovation management, strategic management and future management.

- In innovation management, the research has focused around how companies can develop and/or alter products and processes in order to respond to change and thereby maintain and gain a competitive advantage.

- Within strategic management the focus is on how to plan and implement changes

within the organization.

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- Research within future management aims at finding systematic methods of exploring the future.

The perspective of future management is the area which is most relevant in this thesis, since it involves general actions and abilities to detect and analyse possible future paths, which is why it is further discussed in the following section 2.1.1 Future Management.

From the above mentioned definitions, the keywords detect, discontinuous, explore, strategic and uncertainty were selected. They were chosen because they reflect aspects of change which is included in corporate foresight.

2.1.1 Future Management

The research in future management concerns both micro and macro level of studies.

The macro level focuses on public foresight in national and international organizations while the focus of this thesis is on the micro level of corporate foresight in firms.

Traditionally future management has meant forecasting. However, during the 1990’s the limitations and faults of forecasting became apparent since change does not happen slowly enough for researchers to spot. Consequently, the focus then shifted towards developing possible, probable, plausible and preferable futures, instead of perfectly predicting or measuring future change. One challenge of future management is that in order to add value to firms it needs to be deeply integrated into the process landscape and organizational structures (Rohrbeck, 2011).

Vecchiato (2012) agrees with the difficulties of long-term forecasting and states that it

is possible to predict the short-term and even be fairly accurate when trying to predict

medium term events. The problem arises when companies try to do long-term

predictions since factors, such as the political, social, economic and technical, become

more unpredictable. These difficulties with long-term predictions lead to uncertainties

for companies. There is no method or tool that always work when it comes to long-term

predictions which is why it is important for firms to work with corporate foresight in

order to grasp possibilities and gain long-term advantages (Vecchiato, 2012).

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The importance of a long-term perspective is also mentioned by Horton (1999), who produced a guide of three phases for how to gain advantages from corporate foresight, where each step is more complex than the previous one. The three different phases are Inputs; Foresight and Outputs; and Actions. Through these phases a company will gain additional knowledge for the future and remove information that is not relevant.

However, it is during the third and final step of the model that the value is generated for the company. Therefore, the key aspect of working with corporate foresight is to know in beforehand that the value will not be generated in the short-term, but in the long- term. It is also important to realize the difficulties of actually measuring corporate foresight and its actual contribution. By having this in mind in the beginning it will hopefully lead to less disappointment when not seeing an immediate gain of value from corporate foresight (Horton, 1999).

Based on the three sections above the keyword future was added and uncertainties was further supported.

2.1.2 Value from corporate foresight

Firms in diverse industries such as energy, telecommunication and automotive have discovered the benefits of regularly working with corporate foresight and future oriented techniques (Vecchiato, 2015). Also, several scholars have identified the benefits and impacts of working with corporate foresight. Among those are Becker (2002), who studied 18 companies operating within the EU where he determined the most prominent causes for conducting corporate foresight activities. Through this research, Becker (2002), came to the conclusion that, among other things, corporate foresight is used for: anticipatory intelligence; direction setting; determining priorities;

strategy formulation and implementation; and innovation catalysis. The degree to which

the different companies prioritise these alternatives vary between companies but

indicates the different ways in which corporate foresight can be valuable.

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Another author researching the value of corporate foresight is Rohrbeck (2011), who performed a multiple case study and identified four categories of value creation that corporate foresight yields. A summary of these categories is displayed in Figure 2.

Figure 2: Value created by corporate foresight (based on Rohrbeck, 2011)

The first category of value creation is Reduction of uncertainty. This category contains four factors which are not directly linked to an action being taken by the company, but rather to spread information and awareness throughout the organization. These factors are:

Early warning - enabling to spot faint signals of disruptive change.

Challenge basic assumptions and business logic - challenge the management and emphasize the lack of long-term stability.

Trend identification and interpretation - at both macro and micro level.

Improve decision making - creating additional information leading to better

informed decisions.

The second category of value creation is Triggering internal action and consists of four factors that help to detect threats and seize opportunities early:

Triggering R&D projects - this through initiating and inspiring.

Change of current product portfolio - identify customers’ needs and preferences.

Triggering new business development - Spotting new fields of business that can be explored and developed.

Support strategic decision making - through insights gained from corporate

foresight activities.

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The third category is Influencing others to act, which refer to the fact that the future is not set in stone, but created by actors. This category includes:

Influencing other companies - mainly other actors in the value chain.

Influencing policy making - including public opinion as well as legislation.

The fourth and final value creation category is the Secondary benefits. These are factors where value is indirectly added by foresight activities.

Public relations, marketing and sales - corporate foresight helps these areas strengthen the public image of the company in the eyes of the different stakeholders.

Organizational learning - provide knowledge and increases understanding for terminology.

Rohrbeck (2011) claims that when looking at the value created with the help of foresight, it is not only financial values that need to be considered as there are other values that can be gained as well. However, as Horton (1999) previously mentioned it is common that the benefits are seen in the long run and it is important that this is known beforehand. Otherwise, it might lead to managers expecting short-term returns and therefore not being content with what is accomplished.

Based on Becker’s (2002) research the keywords direction and innovation were motivated to be used as keywords, and the word strategy was further motivated. Looking at Rohrbeck’s (2011) study of value creation from corporate foresight, the selection of the keywords uncertainty, strategic and explore were further motivated. The keywords research, development and opportunity were also found relevant since they are connected to the benefits of foresight.

2.2 Working with corporate foresight

Ruff (2015) mentions knowledge advantages when discussing corporate foresight, but

emphasise the need of specific departments dealing with the gained knowledge and

information, often in-house or closely related to the company. These departments that

work with the unknown and how it should be managed are often called “Crisis

Management Departments”. The difficulties of these departments are that they work in

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a complex environment and face many challenges especially in incorporating the findings into the company's overall strategy (Sapriel, 2003).

In line with Ruff (2015) and Sapriel (2003) is a study discussed by Daheim and Uerz (2008). In the study companies from different industries in Europe with years of experience in corporate foresight were investigated. It became apparent that more than half of the respondents had a separate department within the company, which actively worked with the implementation of foresight. In this study, all the companies had to have a minimum annual turnover of at least €150 million ergo, the companies included were substantially large helping to motivate the resources available to operate this sort of department. If this department did not exist it was common to either have a specific task force or to integrate the department with another one. Meaning that even in these cases there was some sort of specific focus on crisis and future management.

Ruff’s (2015) other alternative was like Daheim and Uerz (2008) to integrate the department into another such as the marketing, innovation or strategy but maintaining separate personnel assigned to these tasks alternatively creating work groups focusing on different projects within corporate foresight. This work would according to Ruff (2015) help companies gain more and deeper knowledge regarding the own company and the logic behind some of the choices and strategies implemented. It would also allow for deeper knowledge of different business units and therefore enable the possibility of working across departmental borders which could possibly lead to competitive advantages.

Despite the positive aspects of corporate foresight, Appiah and Sarpong (2015)

discusses how the implementation of corporate foresight might face some resistance

due to a lack of knowledge, especially regarding a company’s routines, as it might not be

supporting the needed change. They also discuss how there might be struggles in

interpreting signals and spreading the information into other relevant departments, as

well as the possibility of inertia and resistance that comes with all types of change. It is

common that the actions of corporate foresight are seen as time consuming and hard to

actually implement in the organization. Another factor according to Appiah and Sarpong

(2015) is the culture of the company. They claim that the culture of the company will

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highly impact the way in which they handle the different situations. They also claim that the use of routines, if used correctly can help to overcome this. With the added work and processes there is also an increase in the costs of corporate foresight which might face resistance.

Another author discussing working with corporate foresight is Ratcliffe (2006), who stresses the problem corporate foresight faces with the added governance and rules within companies. Through this, it has lost its purpose of being a creative element offering the possibility of foreseeing potential scenarios freely and instead faces the risk of being affected by rules and regulations. To handle this Ratcliffe (2006) has developed some suggestions on how to work with corporate foresight. The first step is that the culture of the company embraces the work with corporate foresight. This is something which is supported by Appiah and Sarpong (2015) but also Rohrbeck (2011) who all discuss the importance of having an embracing company culture and process landscape.

The second step is for a company to envision the change and to realise that there is a problem. This is something which Rohrbeck (2011) mentions as a potential value creator in the first category “Reduction of uncertainty - Challenge Basic Assumptions” in Figure 2. The third step is for a company to explore its creativity. The fourth step is to actively communicate the company's ambitions for the future to all stakeholders. In this aspect, the CEO letter and annual reports are examples of this sort of media. The fifth and final step is that a company should challenge different perspectives. This means to explore future scenarios and from those determine what is needed in terms of abilities and technologies. The goal is to then work towards these futures. Rohrbeck (2011) mentioned something similar when discussing how companies work with possible, probable, plausible and preferable futures which is when the company creates many different options instead of one perfect future.

Keywords further supported from this section were challenges, strategy, innovation and

future since corporate foresight is closely related to a firm’s strategy and challenges but

also its focus on innovation and the future.

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2.2.1 Leadership

As mentioned above, the work with corporate foresight is closely related to the formulation, creation and implementation of the company’s strategy. The Chief Executive Officer, CEO, of a firm is according to Westphal and Fredrickson (2001) someone who is in charge of planning and setting the strategy while also making sure that the necessary actions are implemented in a company. The authors also discuss how the board of the company and the shareholders might impact the CEO, to a varying degree. In some cases, the board and shareholders might not interfere in the CEO’s strategic planning, but simply offer advice, while in some cases they might be highly involved not leaving a lot of room for the CEO to do as she or he pleases.

As previously mentioned, today’s environment is continuously changing and Carnall (2007) discusses the importance of maintaining a strong leader when facing change, challenges and uncertainties. These leaders help the companies work through and survive changes and they play a crucial part in the process. Trust is one of the essential elements of a successful leader. By maintaining a high level of trust throughout their leadership, the employees follow the CEO’s direction, the company’s vision as well as the strategy easier. Other authors who discuss the importance of having a strong leader are Kotler and Caslione (2010), they mention Hermann Simon’s nine lessons when talking about companies who managed to adapt to changing environments. These nine lessons were split into internal, external and core lessons, where the core lessons consisted of having a strong leader and ambitious goals in order to survive the change.

Kotler and Caslione (2010) also discusses, how it can be hard for a leader to remain successful in the aftermath of an uncertain environment, as they might not be able to adapt their leadership style to the new circumstances stressing the importance of having a flexible leader as well.

Based on the literature about working with corporate foresight and the role of leadership,

the keyword leadership was added, as the CEO is the leader of a company and sets the

strategic direction. Keywords that were further supported were: direction, strategy,

challenges and uncertainty.

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2.3 Levels of corporate foresight

As previously mentioned there is lack of research on how to measure, assess and compare corporate foresight in companies and industries. In order to determine different levels of corporate foresight in our chosen industries, previous studies and articles concerned with corporate foresight characteristics were used as a foundation.

In an article from 2015 by Lauren Friedman, she uses patent data from Thomson Reuters database to help determine which industries are the most innovative. This ranking was then used to help rank the six industries used in this thesis: Healthcare;

Technology & Telecom; Industrials; Consumer Industry; Oil, Gas, Materials and Utilities;

and Financials. These are further discussed and motivated in 3.1.1 Company and Industry Selection. Among the top in Friedman’s (2015) article were IT followed by Telecommunications (forming Technology & Telecom). These industries were then followed by the automotive industry (belonging to Industrials), pharmaceuticals and medical devices (belonging to Healthcare). This allows for the conclusion that the most innovative industries in this case are Technology & Telecom followed by Healthcare.

These are then followed by the mid-innovative group including Industrials (chosen since it contains the automotive industry but also several other sectors not placing it in the top tier) and Consumer Industry (this is a sector with many different but small innovation areas seen in the article). Finally, the data from Friedman (2015) shows a small innovation impact in the Oil & Gas industry and none in the Financial, putting these sectors in the least innovative group.

The relationship between innovation and corporate foresight is something Rohrbeck and Gemünden (2011) discuss further. They discuss how important it is to use corporate foresight in order to help enhance the innovation level in companies. Another author discussing this relationship is Becker (2002), who came to the conclusion that one of the main reasons for corporate foresight is to use it as an innovation catalyst.

These authors help stress the relationship between corporate foresight and innovation.

Hence the level of innovation can vary depending on the level of corporate foresight.

Variations in work with corporate foresight in different industries are also affected by

change and technological development. Industries who face a lot of change and

development of new technology should emphasize their work with corporate foresight.

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This can be related to Technology & Telecom as well as Healthcare, which are two of the industries selected for this thesis (see 3.1.1 Company and Industry Selection), as these are industries that are dependent on the development of technology and products (Vecchiato, 2015). Friedman (2015) also mentions Industrials which according to Vecchiato also puts a lot of resources on corporate foresight. However, the sample of industrial companies includes companies within many different fields, hence the degree of working with corporate foresight is assumed to vary between the different companies. Vecchiato (2015) also mentions that industries that are older and have more knowledge regarding the customer preferences spend less time on corporate foresight.

When looking at the six different industries (see 3.1.1 Company and Industry Selection), this includes OGMU and Financials which can be seen as old industries, but it also applies to Industrials. Based on this are the different levels of corporate foresight which can be seen in Figure 3 below. The different industries have been split up into three different groups, where the level of corporate foresight is assumed to increase towards the right.

Figure 3: Levels of corporate foresight

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3 Methodology

This section will explain how the data was assembled and motivate the two chosen methods, content and regression analysis, used to answer the research questions. It will also explain the selection of literature as well as motivate the choice of keywords, financial measurements, companies and industries used in the analyses.

3.1 Research Approach

In order to answer the research questions, an inductive approach was determined to be the best alternative, due to the exploratory nature of the research. Since there is a lack of research on how to measure, capture and compare corporate foresight within individual firms and industries, an inductive approach could help to create theory in this area. Due to the difference of the research questions, two methods for gathering and analysing data were used. To answer the first research question “Which industries focus more on expressing their future orientation externally through the help of their CEO letters from their annual reports?”, a content analysis was performed where keywords, extracted from corporate foresight literature, were searched for in the CEO letter of annual reports. To answer the second research question, “Is there any relation between the amount of corporate foresight keywords and the performance of the firm?” a regression analysis was made using financial data mainly gathered from the data-bases ORBIS but also Retriever Business when data was lacking in the prior. This combination of methods offered a wide base of analysis of the annual reports, as it does not solely focus on the actual keywords, but also puts them in relation to the performance and characteristics of the industries. Making it a good tool for giving an exhaustive answer to the research questions. Both methods will be described further in the following sections.

3.1.1 Company and Industry Selection

The companies used for the sample were selected from the Nasdaq Nordic, large cap

listing. The decision to investigate listed companies was made since it would make it

easier to access the annual reports; the reports comparability increased; and it enabled

potential follow ups with the companies if necessary. By only looking at Nordic

companies, cultural differences of the annual report and language usage were

minimized.

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A number of ten companies were deemed to be appropriate to include per industry in order to get valid results in the study. See Appendices 1-6 for lists of companies from the different industries. This specific number of ten companies per industry was set to make sure that there were enough annual reports in each industry and the process of selecting them was through using a random generator constructed in Microsoft Excel.

Since some industries contained fewer companies a higher number of companies was not possible to set since then it would not be possible to have the same amount of companies in each industry. It was also determined to use ten companies as this would include companies in different fields in each industry. The different industries to be included in the study were:

- Oil & Gas, Materials & Utilities (hereafter OGMU) - Healthcare

- Technology & Telecom - Industrials

- Consumer Goods & Services (hereafter Consumer Industry) - Financials

These industries were selected with the help of the Nasdaq Nordic, large cap as these

were the different sectors which the firms were divided into. The Nasdaq Nordic in turn

uses the Global Industry Classification Standard, GICS which according to Swedbank

(2016) is the classification used on all stock exchanges globally. For the companies to be

comparable only large cap companies, which is when a company has a market

capitalization value of at least 1 billion Euro, were chosen for this study (Swedbank,

2016). This was set as a limitation also due to the fact that corporate foresight often

requires large expenses (Appiah & Sarpong, 2015). By including companies under the

same financial conditions, it is possible that they should in turn have the same

possibilities of working with future orientation through corporate foresight. In total the

Nasdaq Nordic large cap contained 195 companies when the list was accessed on the

7th of February 2017. However, some industries contained fewer companies and since a

criterion of 10 companies in each industry was set, industries with less than 10

companies were therefore merged with a similar industry. This was the case for OGMU,

(consisting of Oil & Gas, Materials and Utilities); Technology & Telecom; as well as the

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Consumer Industry (consisting of Consumer Goods & Services). By combining these, this resulted in a total of six different industries which are mentioned in the list above.

3.1.2 Company Sample

In some of the industries there were more than ten companies present. In these cases, a random generator was constructed in Microsoft Excel to help select the ten companies that should be used in the study. If the needed data from any of the first ten companies was not available, because it could not be accessed in English for all the required years or that there were some other problems, the next company in a numerical order was selected until there were ten viable companies per industry.

To be able to generalize valid results a large sample was needed and therefore an interval of five years of annual reports were deemed to be sufficient. This decision was made to allow for a large enough sample of annual reports to help draw conclusions from the material collected. The five year interval was also motivated as it would possibly remove biases in language during a specific year and thereby provide a more stable measure. For a company to be valid with regards to the keywords they had to present annual reports for five years, 2011-2015. To assemble the financial data the database ORBIS and Retriever Business was used and data for 6 years, the years 2010- 2015, was collected. The reason for this was to be able to analyse the whole year of 2011. By including one more year, 2010, of financial data it was also possible to investigate delayed effects of the performance on the corporate foresight keyword frequency. These specific set intervals were selected for all companies in case there was a specific event that occurred during this time as it would then hopefully affect them to the same extent. These years were also chosen to increase the relevance of the study by choosing up-to-date reports. The choice to not include 2016 was made as it might be hard for companies to have assembled and completed these annual reports due to the proximity of the year end. However, if there was data absent for some financial variables for certain years and companies, this did not exclude the company in question but it was decided to include all the data available to get as large a sample as possible.

After this the different financial reports were collected. In total, there were a number of

300 CEO letters from annual reports included as there were six different industries,

annual reports from five years per company and ten companies per industry.

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3.2 Content Analysis

The first part of the research was a content analysis conducted through a keyword search in order to answer the first research question “Which industries focus more on expressing their future orientation externally through the help of their CEO letters from their annual reports?”. According to Duriau, Reger and Pfarrer (2007), the way in which different words are used and how these are continuously changed demonstrates how the focus and attention of an organisation shifts. A content analysis would therefore help to demonstrate a company’s intentions and focus. Bryman and Bell (2015) states that the use of content analysis has two main advantages. Firstly, it is objective since the assembling of data follows certain procedures and therefore it is not impacted by the authors. The second main advantage is what Bryman and Bell (2015) describe as systematic, which refer to the pre-set procedure that makes replicability easier in a content analysis compared to other methods. Duriau et al. (2007) agrees that there are advantages regarding the replicability when working with content analysis. They also state that analysing content gives better access to individual and personal beliefs such as values and ideals. Woodrum (1984) also points out that in comparison to other methodologies, it is a reasonably safe way of collecting data as it allows for the authors to be able to analyse it again if necessary. Bryman and Bell (2015) mentions that the use of content analysis, when examining the content of annual reports, is something which is fairly common as it helps to capture relevant details in a swift manner. This helps to further motivate the selection of this method.

However, there is also resistance to content analysis. Firstly, it is highly dependent on

the quality of the documents which it analyses. Secondly, it is hard to completely avoid

any biased opinions as the coding is open for interpretation by the authors. Bryman and

Bell (2015) also stresses that it might be hard to develop a deeper analysis with the data

assembled through the content analysis. However, through the second research

question the sole focus of this thesis is not on the content analysis but rather on two

different approaches adding value to the end-result. The two above mentioned factors

of resistance were tried to be avoided by using a software for collecting the data and by

collecting data from annual reports. A concern with annual reports was the potential

bias, which is something Duriau et al. (2007) discusses, since the annual report is

created by the company itself. The words that the companies use to help describe their

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goals and business are chosen to put them in a positive light, since the report is made to make the firm look good in the eyes of the stakeholder’s. However, since it is very likely that this is common among all companies the reports still make an equal comparison between the selected industries. Further, the financial data in the report must be approved according to regulations. When collecting data through a content analysis, there is always a risk of it being biased, however, data gathered using alternative methods could also include some sort of bias making this of less importance. The basis for analysis in this thesis is the CEO letter and according to Amernic, Craig and Touris (2010) the CEO has a legal responsibility to make sure that the content in this letter is correct. Based on this and the motivation of the annual report as a form of communication in the introduction, the annual report is believed to be a good way of assessing an organization’s externally communicated corporate foresight, while also bearing the limitations in mind.

3.2.1 Keywords

In order to assess corporate foresight and future orientation in the CEO letters, a number of keywords were selected on the basis of them being relevant in the corporate foresight literature. The selection of keywords was made by the authors through a scan of the assembled literature. Where in the literature the keywords were found is mentioned in 2 Literature. The literature scan resulted in the selection of the keywords displayed in Table 1 and Table 2. Keywords which were hard to judge if they were relevant or not were searched for in a sample of the annual reports to see in which context they were mentioned. This was then the basis for including or excluding them in the analysis. Keywords that were excluded after the search in annual reports were:

change, communication, creativity, culture, disruptive, long-term, patent, respond, value

and vision. In order to make sure that different formulations and expressions of a

keyword were included, similar words which were related to the ones from the

literature were added as well, called connecting keywords. In Table 1 the keyword

collected directly from the literature is displayed first, followed by the connecting

keywords and a motivation for the selection.

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Keywords

Original word Connecting keywords Motivation

Detect

Development Growth, Performance Other words for describing a company’s development.

Discontinuous Direction Explore Future

Innovation Launch It indicates how a company is innovating for the future.

Leadership Opportunity

Research

Strategy Acquisition, Expansion,

Investment These words help to show the strategic activities undertaken by a company.

Uncertainty Challenge, Turbulence Connected to uncertainty as they demonstrate disruptions in the external environment that a company faces.

Table 1: Keywords, Connecting keywords and Motivations

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3.2.2 Data Assembling of Keywords

NVivo, which is a qualitative data research software was then used to help compare the different annual reports. This is a tool which is mentioned as being efficient by Bryman and Bell (2015) as it allows for the user to quickly filter through large amounts of text and data in search of specific keywords. As this aligns with the content analysis, NVivo was deemed to be a suitable program when conducting this part of the data collection.

With the help of NVivo, all the chosen keywords were searched for in the companies’

annual reports and the word frequency in terms of the number of mentions in the CEO

Letter was noted. To avoid certain keywords being excluded due to different stems, a

feature was selected to include all words with the same root. For example, the word

challenge would also include challenging in the word count. Some conjugations of the

keywords were excluded since they were often or all of the time mentioned in the

wrong context. The keywords used in the analysis, which conjugations were included

and which were excluded can be found in Table 2 below.

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Keywords for Data Collection

Keyword Included Excluded

Acquisition(s)

Acquire, Acquired, Acquisition-based

Challenge(s)

Challenge, Challenged,

Challenging

Detect

Development(s)

Develop, Developed,

Developing Developer

Direction(s)

Discontinuous

Discontinue, Discontinued,

Discontinuation

Expansion(s)

Expand, Expanded,

Expanding

Explore

Explored, Exploring

Future

Growth

Grow, Grew, Grown, Growing

Innovation(s)

Innovative, innovate, Innovating, Innovation- driven

Investment(s)

Invest, Invested, Investing

I

nvestors

Launch(es)

Launched, Launching

Leadership

Leader(s) Lead, Leading

Opportunity(s)

Performance

Perform

Research

Strategy(s)

Strategic

Turbulence

Turbulent

Uncertainty(s)

Uncertain

Number of keywords = 20

Table 2: Keywords for data collection

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The results were then split up in relation to the different industries to see if there were any common traits in regard to these groups. This data was then used to compare the industries with each other to see if the keywords that were related to corporate foresight were used more in one industry than another, or if there were words that were not mentioned in any industry but highly relevant in the literature. To help compare the word frequency and percentage, Microsoft Excel was used to assemble the results and to then compare these between the different industries.

3.3 Regression Analysis

In order to answer the second research question “Is there any relation between the amount of corporate foresight keywords and the performance of the firm?” and look further into corporate foresight, it was decided to conduct a research focusing on the performance of the firm in terms of growth, performance, innovation and flexibility. The performance was investigated in relation to corporate foresight and a number of financial variables were collected, which will be presented further down in the section 3.3.1 Financial Variables. With the help of financial data from the various companies a more in depth research regarding the effects on corporate foresight could be studied.

Through using the databases ORBIS and Retriever Business, the financial data was extracted from the annual reports of the companies. As discussed earlier, the company's’ legal responsibilities regarding annual reports made them a suitable choice for the financial data collection.

A regression analysis was then performed to analyse the financial data. According to

Cohen, Cohen, West and Aiken (2013) the purpose for a regression analysis is to

discover any relationships between a dependent and independent variables. In this

case, the dependent variable was the amount of corporate foresight keywords and the

independent variables were the financial measurements. Correlation is the relationship

between the variables, ranging on a scale of 1 to -1. A correlation of 1 represents a

perfect positive relationship, while -1 represents a perfect negative correlation between

the variables. A correlation of 0 shows that there is no correlation at all between the

variables. The different variables are then placed somewhere along this scale helping to

indicate the relationship between the independent and the dependent variable. The

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value in turn helps to show how much the dependent variable would either increase or decrease if the independent were to change by one unit (Cohen et al., 2013).

When performing a regression analysis Bryman and Bell (2015) discusses the significance, which is the chance of a specific event to occur when looking at the correlation of a random sample. The lower the significance, the higher chance of it getting the desired result from a sample. In this research significance was not prioritised as the goal was to look upon the relationship between the variables and the main purpose of using the significance is to either reject or approve a hypothesis. Two factors that affect the significance are the size of the calculated coefficient and the sample size, the larger the sample, the more likely it is significant (Bryman and Bell, 2015). To determine if the variables could be used, the Variance Inflation Factor, VIF, was looked upon. According to O’Brien (2007) VIF is a measurement used to describe the multicollinearity of the independent variables with each other. Even though O’Brien (2007) states that a VIF value over 10 should not be discounted it is preferred if the VIF value is less than 10, indicating a suitable level of multicollinearity.

3.3.1 Financial Variables

The second step of the data assembling and analysis focused on the financial variables for the companies in the chosen industries. With the help of the database ORBIS and Retriever Business, data was assembled for the period, 2010-2015. The reason for including 2010 as an additional year was in order to measure the changes throughout the years. Another reason was to be able to see if the financial variables gave a delayed effect on the keywords. The different financial measurements were:

- Number of Employees

- Earnings before interests and tax (hereafter EBIT)

- Research & Development Intensity (hereafter R&D Intensity) - Current Ratio

These variables were chosen since they represent different characteristics of a firm in

terms of growth, performance, innovation and flexibility. This will be explained further

below in 5.2.1 Financial Variables. The growth and size of the firm is related to the

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Number of Employees, which is the first measurement, as it can indicate growth or cut down of the firm. The second measurement, EBIT, was chosen since it shows the performance and profitability of a company. The measurement R&D Intensity was added as a third measurement, in order to show the money invested in R&D in relation to the turnover of the company. Meaning that R&D Intensity shows firm’s focus on innovation. To calculate the R&D Intensity, R&D expenses and turnover were extracted from the annual reports of the companies with the help of the database ORBIS and Retriever Business. R&D Intensity was then calculated with the following formula:

R&D Intensity = R&D Expenses/ Turnover

Since R&D expenses is related to the size of the company, R&D Intensity allowed to better see how much focus a company has on R&D and innovation. Showing what percentage of their turnover they use to continue to develop rather than only looking at numbers without accounting for the size of the firm.

The fourth and final financial variable was Current Ratio, which according to Johansson, Johansson, Marton and Pautsch (2013), shows the liquidity of a firm. Current Ratio shows the ability of a company to pay potential debts throughout the current year. This indicates a company's level of flexibility when facing change. To calculate the current ratio the following formula was used:

Current Ratio = Current Assets/ Current Liabilities

3.3.2 Conducting the Regression Analysis

To determine the relationship between the corporate foresight keywords and the financial variables, a regression analysis was conducted. The measure for the keywords was their percentage out of all the words in the CEO letters. The keyword percentage was set as the dependent variable since it was expected to be affected by the financial variables, which were the independent variables.

This was then followed by conducting an OLS regression, commonly known as a linear

regression analysis, which was made in SPSS for all industries and then for each

industry separately. This was done to be able to separately analyse the industries and

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their relationship to their individual keyword percentage. To conduct an OLS regression was further motivated as the dependent variable, the keyword percentage of corporate foresight, showed a low level of standard deviation (Table 5 & 6) indicating that the values are close to the mean. The OLS regression was done to determine if there were any relationship between the keyword percentage and the industries in terms of innovation, future focus, development, growth, size, performance and flexibility, variables which are all related to corporate foresight.

For the Financials, the values for R&D, used to calculate R&D Intensity, were missing both in the database ORBIS, Retriever Business and in the companies’ annual reports.

This is most likely due to the fact that most financial companies do not perform any formal R&D or that they do not account for it as a separate expense in their annual reports. Due to this, values for R&D Intensity were set to zero for Financials indicating significant but irrelevant values. However, since R&D in an annual report is a measure of formal R&D it does not take all development activities into account. R&D is an appropriate measure for some industries while for Financials it might not display an accurate value of their amount of development activities. Therefore, setting the R&D Intensity to zero for Financials might cause bias in the regressions since Financials represent one sixth of the observations. To see if this bias disrupted the results or not a robustness check of the regression analysis was made where the values for Financials were removed. The results without Financials showed minimal changes in the correlation, regression and significance and therefore R&D Intensity for Financials were kept in the analysis.

3.4 Theoretical Framework Assembly

As a base for the thesis, a theoretical framework was assembled with the use of a

systematic review. This is a method described by Bryman and Bell (2015) and Jesson,

Matheson and Lacey (2011) which helps to avoid biased literature being selected which

might only support the preferred opinions of the authors. This is achieved by using

among other things inclusion and exclusion criteria. It is also said that this method

allows for a more solid evidence base creating more trust in a study. However, this

approach might be harder to use when there is a less defined subject and it is also hard

to determine the quality of the included publications according to Bryman and Bell

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(2015). In the systematic review the words displayed in Appendix 7 were assembled and searched for in order to find the relevant literature. These words were chosen based on the relevance in regard to the study and then with the help of looking at the keywords used for similar literature, some extra words were added.

3.4.1 Inclusion and Exclusion Criteria

When conducting the systematic review a few inclusion criteria were added to help determine the requirements for this thesis. These were that the theory collected should be applicable to all firm sizes and organization structures. It was also decided that the theories should be generalized to all industries or that the theory should be specifically for our chosen industries. All literature should also be within the business field to help motivate their relevance. As part of the systematic review there were also some exclusion criteria added. The literature should not focus on specific foresight methods, for example road-mapping and scenario analysis, but that it should instead focus on corporate foresight in general. This was also applied for literature for the annual reports as well as for the theories regarding leadership and CEOs. It was also decided that literature that was not written in English or Swedish should be excluded as translation of other languages might cause misinterpretations.

3.4.2 Literature Credibility

To make sure that the collected literature was credible the university’s library database and Google Scholar was used when searching for relevant literature. In order to make sure that the selected literature maintained a high quality, the number of citations helped to show the trustworthiness of the articles. Another action that was taken to make sure that the literature maintained high quality was to use validated journals.

3.5 Research Quality

According to Bryman and Bell (2015) a study has to be reliable. By this they mean that

the study has to be able to be conducted again. Problems with reliability is often more

common when conducting a qualitative study and as this thesis mainly had a

quantitative focus, repetition was something that should be possible. By using a

software for the content analysis, the reliability was believed to be high, since faults in

counting and calculations were minimized. However, the factor of human errors can

never be fully disqualified. To further minimize mistakes in the data collection the

References

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