Artificial Intelligence and its Breakthrough in the Nordics
A Study of the Relationship Between AI Usage and Financial Performance in the Nordic Market
Frida Ottosson, Martin Westling
Department of Business Administration Master's Program in Finance Master's Thesis in Business Administration II, 15 Credits, Spring 2020 Supervisor: Catherine Lions
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ABSTRACT
As the fourth digital revolution is initiated and digitalization is becoming increasingly evident in today’s society, the concept of artificial intelligence (AI) is experiencing a boom and is continuously transforming a vast variety of industries. Previous studies have found several links between AI usage and economic benefits, such as increased efficiency and lower costs. Furthermore, such benefits have been connected to financial performance indicators such as return on assets (ROA) and stock return. Additionally, the Nordic countries are known for their flourishing technological environment and the involvement in well-known technology-oriented companies. These underlying factors shaped the interest of exploring the relationship between AI usage and financial performance, as measured by ROA, stock return and the volatility of stock returns. The idea of including these three performance indicators was to get both an internal perspective, as well as a market perspective from an investors point of view while incorporating risk. This census study was conducted by performing three multiple regression models on the companies on Nasdaq OMX Nordic, which resulted in a population of 152 companies. By gathering observations between the years 2015-2019, the total number of observations amounted to 721 for the ROA model, 720 for the stock return model, and 714 for the risk model.
The study follows a quantitative research design, with an objective and positivist view in regard to the research philosophical assumptions. Furthermore, a deductive research approach is taken, since previous studies, as well as theories such as the stakeholder and shareholder theories, the disruption theory, the resource-based theory and the dynamic capabilities theory are used to make conclusions. Additionally, the chosen regression model was the OLS model, incorporating the robust function since none of the regressions were fulfilling the assumption of constant variation of the error term.
On a 95% confidence interval, all null hypotheses could be rejected, meaning that there was a relationship between AI usage and all performance indicators. However, the relationships were unexpectedly weak and opposing of the researchers’ expectations. As it turns out, internal performance as measured by ROA, as well as market performance measured by stock return proved to have a small negative relationship with ROA. This means that Nordic companies utilizing AI sees a negative impact on financial performance in the short run. However, risk as measured by the standard deviation (SD) of stock returns, showed a positive relationship with AI usage, meaning that investing in companies using AI is riskier. The findings contradict the idea that the economic benefits from AI cause a higher financial performance. However, since AI is just seeing a boom as of recently, it is possible that it might pay off financially in the long run.
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ACKNOWLEDGEMENTS
We would like to thank our teacher and supervisor Catherine Lions for her wonderful support during the writing of this thesis. She has been very helpful and constantly available throughout the whole process, even considering the unfortunate Covid-19 situation.
Frida Ottosson Martin Westling
Umeå 2020.05.27
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TABLE OF CONTENTS LIST OF TABLES ...... vii LIST OF FIGURES ...... vii LIST OF EQUATIONS ...... viii LIST OF ABBREVIATIONS ...... viii
1. Introduction ...... 1 1.1 Background ...... 1 1.2 Problematization ...... 2 1.3 Research Question ...... 4 1.4 Research Purpose ...... 4 1.5 Contributions ...... 5 1.5.1 Theoretical Contributions ...... 5 1.5.2 Practical Contributions...... 5 1.6 Delimitations ...... 6
2. Theoretical Framework ...... 7 2.1 The History and the Future Prospects of Technology ...... 7 2.1.1 The History and the Future Prospects of AI ...... 9 2.2 Challenges of AI ...... 11 2.2.1 Cyber Risks ...... 11 2.2.2 Societal Impacts of AI...... 13 2.2.3 AI´s Impact on the Workforce ...... 14 2.2.4 Operational Costs of Implementing New Technologies ...... 15 2.3 Artificial Intelligence in Different Industries ...... 16 2.3.1 Food Industry ...... 17 2.3.2 Healthcare Industry ...... 17 2.3.3 Automotive Industry ...... 18 2.3.4 Financial Industry ...... 19 2.3.5 Energy Industry ...... 20 2.3.6 Supply Chain Management ...... 20 2.4 Financial Performance ...... 21 2.4.1 Return on Assets (ROA) ...... 21 2.4.2 Stock Return ...... 22 2.4.3 Standard Deviation of Stock Returns ...... 22 2.5 The Stakeholder and Shareholder Theories ...... 23 2.5.1 The Shareholder Theory ...... 23 2.5.2 The Stakeholder Theory ...... 24 2.5.3 The Stakeholder and Shareholder Theories in Relation to AI Implementation ...... 25
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2.6 Disruption Theory ...... 26 2.7 Resource-Based Theory ...... 27 2.8 Dynamic Capabilities Theory ...... 29 2.9 Summary of the Theoretical Framework and Hypotheses ...... 30
3. Scientific Method ...... 32 3.1 Choice of Subject ...... 32 3.2 Preconceptions ...... 32 3.3 Research Process...... 32 3.3.1 Research Philosophy ...... 33 3.3.2 Ontological Assumptions ...... 34 3.3.3 Epistemological Assumptions ...... 35 3.3.4 Research Design and Methodology ...... 35 3.3.5 Research Strategy ...... 36 3.3.6 Time Horizon ...... 38 3.3.7 Research Approach ...... 38 3.4 Literature Search Process ...... 40 3.5 Source Criticism ...... 40 3.6 Ethical and Social Considerations ...... 40
4. Research Method ...... 42 4.1. Census Study ...... 42 4.2 Data Collection...... 42 4.3. Variables ...... 43 4.3.1 The Dependent Variables ...... 43 4.3.2 Independent Variable ...... 44 4.3.3 Extraneous Variables ...... 44 4.4 Regression Analysis ...... 45 4.4.1 Coefficients ...... 46 4.4.2 Dummy Variables ...... 46 4.4.3 Ordinary Least Squares (OLS) ...... 46 4.5 Hypotheses...... 47 4.6 Regression Models ...... 48
5. Data ...... 50 5.1. Descriptive Statistics ...... 50 5.1.1 Two-Sided T-test ...... 50 5.1.2 Correlation Matrix ...... 52 5.2 Market Distributions ...... 53 5.3. Ordinary Least Square Assumptions ...... 55
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5.3.1 Linearity (Assumption 1) ...... 55 5.3.2 Error Term (Assumptions 1, 2, 3 & 7) ...... 57 5.3.3 Autocorrelation (Assumption 4) ...... 59 5.3.4 Heteroscedasticity (Assumption 5) ...... 59 5.3.5 Multicollinearity (Assumption 6) ...... 60 5.4 Final Regression Models ...... 61
6. Results ...... 64 6.1 Multiple Regression Model of ROA (OLS Robust) ...... 64 6.1.1 ROA and AI ...... 64 6.1.2 ROA and Market Capitalization ...... 65 6.1.3 ROA and Country ...... 65 6.1.4 ROA and Industry ...... 65 6.2 Multiple Regression Model of Stock Return (OLS Robust) ...... 66 6.2.1 Stock Return and AI...... 67 6.2.2 Stock Return and Market Capitalization ...... 67 6.2.3 Stock Return and Country ...... 67 6.2.4 Stock Return and Industry ...... 67 6.3 Multiple Regression Model of Risk (OLS Robust) ...... 68 6.3.1 Risk and AI ...... 68 6.3.2 Risk and Market Capitalization ...... 69 6.3.3 Risk and Country ...... 69 6.3.4 Risk and Industry ...... 69 6.4 Hypothesis Summary ...... 70 6.5 The Truth Criteria ...... 70 6.5.1 Validity ...... 70 6.5.2 Reliability ...... 71
7. Analysis and Discussion...... 73 7.1 Financial Performance and AI Usage ...... 73 7.1.1 ROA ...... 74 7.1.2 Stock Return ...... 75 7.1.3 Risk ...... 75 7.2 Analysis of The Extraneous Variables ...... 76 7.2.1 Size ...... 76 7.2.2 Country ...... 76 7.2.3 Industry ...... 77
8. Conclusions and Future Research ...... 78 8.1 Conclusions ...... 78
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8.2 Contributions and Implications ...... 79 8.2.1 Practical Contributions, Societal and Ethical Implications ...... 79 8.2.2 Theoretical Contributions ...... 80 8.3 Limitations and Future Research ...... 81
References ...... 83
Appendix 1...... 93
LIST OF TABLES
Table 1. Descriptive Statistics ...... 50 Table 2. T-test ROA ...... 52 Table 3. T-test Return ...... 52 Table 4. T-test Risk ...... 52 Table 5. Correlation Matrix ...... 53 Table 6. Heteroscedastic Test ROA ...... 59 Table 7. Heteroscedastic Test Return ...... 59 Table 8. Heteroscedastic Test Risk ...... 59 Table 9. VIF Test ROA...... 60 Table 10. VIF Test Return ...... 61 Table 11. VIF Test Risk ...... 61 Table 12. Multiple Regression Model ROA ...... 64 Table 13. Multiple Regression Model Stock Return ...... 66 Table 14. Multiple Regression Model Risk ...... 68 Table 15. Hypothesis Testing ...... 70
LIST OF FIGURES
Figure 1. Growth Scenarios For the U.S Economy ...... 8 Figure 2. Artificial Intelligence and its Subgroups...... 9 Figure 3. Relation Between Digital Transformation Strategy and Corporate Strategies ...... 16 Figure 4. Stakeholder Map ...... 25 Figure 5. The Process of this Research ...... 33 Figure 6. The Deductive Process ...... 39 Figure 7. Histogram of ROA and AI ...... 51 Figure 8. Histogram of Return and AI ...... 51 Figure 9. Histogram of Risk and AI ...... 51 Figure 10. Industry Distribution ...... 53 Figure 11. Country Distribution ...... 54 Figure 12. Percentage of Companies Using AI Between 2015-2019 ...... 54 Figure 13. Growth of Number of Companies Using AI Between 2015-2019 ...... 55 Figure 14. AI Implementation for All the Observed Years ...... 55 Figure 15. Two-Way Scatterplot of ROA as Fitted Values Plotted Against Residuals ...... 56 Figure 16. Two-Way Scatterplot of Return as Fitted Values Plotted Against Residuals ...... 56 Figure 17. Two-Way Scatterplot of Risk as Fitted Values Plotted Against Residuals ...... 57 Figure 18. Histogram ROA ...... 58 Figure 19. Histogram Return ...... 58 Figure 20. Histogram Risk ...... 58
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LIST OF EQUATIONS
Equation 1 ...... 45 Equation 2 ...... 46 Equation 3 ...... 48 Equation 4 ...... 48 Equation 5 ...... 49 Equation 6 ...... 62 Equation 7 ...... 62 Equation 8 ...... 63
LIST OF ABBREVIATIONS
(AI) Artificial intelligence (AIC) AI-crimes (ANN) Artificial neural network (CPU) Central processing units (CSR) Corporate social responsibility (EPS) Earnings per share (GE) General electric (GPU) Graphical processing unit (HR) Human resources (IT) Information technology (ML) Machine learning (NPL) Natural language processing (OLS) Ordinary least squares (RPA) Robotic process automation (R&D) Research and development (ROA) Return on assets (ROE) Return on equity (SD) Standard deviation (TFP) Total factor productivity
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1. Introduction
This first chapter will present the background and the relevance of the topic, as well as the problematization that has been formulated from previous literature. Subsequently, the research question and purpose will be presented. Lastly, the expected contributions and delimitations will be discussed.
1.1 Background
The digital reality is characterized by innovation accelerating at a growing speed. Cycles of new product innovation are getting shorter, the world has become more interconnected and venture capital is more easily available than ever before. Traditionally, established companies with long traditions and successful histories are chasing value, some competing to survive in the diverse ocean of business of new agile startups. Intangible and knowledge-based assets are crucial in order to thrive in the modern marketplace. Many businesses are today characterized by transformation and aiming to integrate digital technology in order to stay competitive (Tekic & Koroteev, 2019, p. 684). Taking such initiatives impact companies to a large extent, and involve complex transformations in core business operations, structures, management approaches as well as products, sales channels, and supply chains (Matt et al., 2015, p. 341).
In recent years, companies within most industries have taken actions to explore new digital transformation areas and the entailed benefits. Applying new technology can have many potential benefits such as increasing effectiveness and sales, improving customer interaction, new product innovation, cost cutting and all kinds of value creation (Matt et al., 2015 p. 341). Furthermore, the Nordic countries have been shown to be at the forefront of technological innovation, with involvement in companies such as Spotify, Skype, King (Candy Crush) and Rovio (Angry Birds) (Chapman, 2018 ; Cook et al., 2015). One of the hottest topics when it comes to new technology is artificial intelligence (AI). The concept of AI has a long history, stretching for half a century. However, AI has recently experienced a boom, and is frequently used in today’s society even though it is not always commonly known (Bini, 2018, p. 2359; Garbuio and Lin, 2019, p. 63).
The general perception of AI is grounded in science fiction with robots living alongside humans, engineered to perfection beyond human capacity. Famous examples such as Star Wars, Interstellar, The Matrix, Westworld and Terminator are stories building on romanticized and highly developed AI (Hogan & Whitmore, 2015 ; Beckenlehner, 2017). However, even though AI has not reached such a level in reality, the progress has advanced rapidly in the 21th century (Marr, 2019b). Some examples include self-driven cars, Apple's Siri, Faceid, Amazon's Alexa, Microsoft's Cortana, Google duplex, Google translate, and Spotify's algorithm of finding your favorite songs. These technologies are creating products, as well as improving businesses and systems which are omnipresent in all sectors. AI is built upon algorithms, giving the software input of data which it will learn from (Bini, 2018, p. 2359).
AI is used to improve life by increasing efficiency, creating conveniences, and replacing tedious tasks with machines. The fourth industrial revolution is initiated, and the practical benefits of AI is something that society and the corporations have embraced in the past years (Oztemel & Gurzev, 2018, p. 127). Accenture has for instance found that banks
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investing in AI and putting emphasis on cooperation between employees and machines can increase revenue by 34% between 2018 and 2022 (Shook et al, 2018, p. 3). It is estimated that as much as 30 billion USD was spent on AI between the years 2013 to 2018 (Bini, 2018, p. 2358). It is therefore important for companies to understand and learn the features and implications of the potential transformation of how machine dominant approaches are changing to digital solutions. The opportunities of AI might be infinite and there is a lot of value that businesses can achieve from following this trend (Oztemel & Gurzev, 2018, p. 127). The evolution of AI is seen to have a long vision for the future, which is both welcomed by anticipation, but also with concern. It is currently unknown how smart the machines might become. For example, whether they will obsolete human labor to a high degree, or even start expressing and recognizing human emotions (Bernabe, 2018). Additionally, it is predicted that the effects related to this transformation will have a huge impact on social life, entailing several societal implications (Oztemel & Gurzev, 2018, p. 127). For example, the loss of jobs leading to transformation of labor demographics, human psychological impacts and cyber risks (Frank et al., 2019; Taddeo, 2018; King et al. 2018).
1.2 Problematization
As the fourth industrial revolution is initiated, the importance of companies transforming to new digital solutions is crucial in order to stay competitive (Oztemel & Gurzel, 2018, p. 127). The concept of AI has existed since 1956 but has today advanced and is widely used in companies throughout all industries, although still in an early phase (Bini, 2018, p. 2359; Garbuio and Lin, 2019, p. 63). AI has been referred to as a disruptive technology (Wheeler, p. 66, 2020), which according to Christensen's disruption theory (1997, p. 218) is a game changing innovation that will transform industries and markets. AI makes it possible to access and analyze a huge amount of data beyond human capacity (Wheeler, p. 66, 2020). It is already possible for AI to perform standardized tasks to some degree, however the technology will reach more advanced levels in the future (Wheeler, p. 66- 70, 2020).
A study conducted by Purdy and Daughtery (2016, p. 3) showed that AI is likely to increase workers’ productivity by almost 40% by 2035, as well as doubling the rates of economic growth (Purdy & Daugherty, 2016, p. 3). In the same study, Sweden and Finland showed the highest potential of increased labor productivity due to AI, with figures of 37% and 36% respectively (Purdy & Daugherty, 2016, p. 17). Moreover, AI has contributed with several benefits throughout various industries, leading to increased productivity and economic benefits. Another study performed by Soltani-Fesaghandis & Pooya (2018, p. 848) showed that in food industries, AI systems can be used in the decision-making process of launching new products. It is an area where the technology contributes a lot in terms of savings optimization, since 90% of food products tend to fail within the first year of launch (Soltani-Fesaghandis & Pooya, 2018, p. 848). Moreover, AI is highly relevant in the healthcare industry where it has a revolutionary impact (Garbuio and Lin, 2019, p. 59). AI has contributed to lowering costs, accelerated drug discovering and improving outcomes (Garbuio and Lin, 2019, p, 59). Furthermore, the automotive industry is facing major changes due to AI. Self-driving vehicles are predicted to have the potential of decreasing the American road accidents by 90% (Soegoto et al., 2019, p. 5). It is also predicted that car-hailing might roughly replace the need of purchasing a car by 2030 as a direct result of AI (Garfield, 2017). Self-driving cars
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include benefits such as time efficiency, increased safety, as well as opening up opportunities to perform other activities whilst in the car (Soegoto et al., 2019, p. 5).
AI is predicted to transform the financial sector. By 2030, insurance companies, investment firms and banks can save $1 trillion USD by implementing AI technologies into their business models (Wheeler, p. 67, 2020). Regarding supply chain management, the freight company DHL is using machine learning (a form of AI), to predict flight delays and potential problems of delays. This is helpful in knowing when to deliver and which airline to use (Gesing et al, 2018; Banker, 2019, cited in Hartley and Sawaya, 2019, p. 710). Bottani et al. (2018, p. 698) found that using AI can decrease economic losses of out-of-stock situations by 56%. Furthermore, AI has huge benefits in the energy industry by cutting costs and emphasizing sustainability. Additionally, a recent study by Banga (2020) has predicted that by 2024, the use of AI in this industry will amount to 7.78 billion USD, which would equal a compounded annual growth rate of 22.49% from 2019 (Banga, 2020).
There are several perspectives regarding how AI will impact the workforce. From one perspective, AI will be able to work synergically with humans, leading to increasing productivity. Another perspective is that workers might be replaced by machines in a vast variety of sectors and tasks (Frank et al., 2019, p. 6531). The fact that AI is changing the demographics of the labor market might also create new kind jobs. This has stressed the value of technological competence, creativity and the ability of employees being open to adopt of new skills. This transformation is seen as the largest challenge in the implementation of AI, which creates a large importance of the organizational management (Wheeler, p. 69-70, 2020). Proper management of human resources is crucial in order to achieve a sustained competitive advantage for firms (Barney & Clark, 2007, p. 141). According to Hartley and Sawaya (2019, p. 714) this is a process that has to be planned in order to decrease technological transformation barriers. Therefore, the real industrial transformation of AI may still be years away. In the area of financial markets, many organizations have already undertaken implementation of AI. However, a mix of the most developed technology in combination with talent is seen as the most important factors in order for organizations to continue blooming (Wheeler, p. 69-70, 2020). Furthermore, the implementation process of AI is seen as a challenge to many organizations. Implementing new technologies can have outcomes such as core business transformations, conversions of managerial approaches, implementation of new business strategies and changes throughout whole organizations (Matt et al. 2015, p. 341). New technology entails many opportunities, however, these potentials also have to be outweighed by the associated challenges (Floridi et al, 2018, p. 291).
The resource-based theory is used to explain long term financial performance of firms, and this is dependent on how company resources are utilized (Barney & Clark, 2007, p. 17). Companies already involved in a certain area or are large with many resources have an increased likelihood of achieving a sustained competitive advantage (Barney & Clark, 2007, p. 46-47). The dynamic capabilities theory is embracing the strategy of adopting new areas of opportunities in an early phase (Kump et al., 2019, p. 8 ; Chukwuemeka & Onuoha, 2018, p. 11). The theory is also emphasizing that based on the resources a corporation dwells, management should organize and spend them in order to capture the opportunities that are the most suitable in regard to the business operations (Kump et al., 2019, p. 9). This means that corporations in every industry might be able to formulate and take advantage of the opportunities of AI, as long as they do it in the way that best suits their operations. Another viewpoint is the importance of internally transforming the
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organization and being constantly prepared for new market changes (Kump et al., 2019, p. 10). Human resources are also an important element of the resource-based theory. Organizational culture and values can have positive impacts of optimizing value creation over competitors (Barney & Clark, 2007, p. 79).
From a shareholder’s perspective, the different dimensions of implementing AI in various sectors have a great potential to increase shareholder value. This is because the economical benefits from AI implementation can be linked to financial performance. The reason behind this is that factors such as cost reduction and increased efficiency is connected to a higher profitability, which in turn is related to a higher financial performance (Fatihudin et al., 2018, p. 554) On the other hand, from a stakeholder’s perspective, AI might create value to society by new innovations that save resources, as well as improving human life. However, there is some controversy on the impact on some stakeholder groups, since it might for example cause unemployment.
To conclude, there is a large amount of literature and practical examples of how AI causes economical contributions in several areas. This in addition to the fact that Nordic countries are known as early adopters when it comes to technology, makes it interesting to study AI in terms of financial performance in the Nordics. Financial performance is explained as a firm’s financial condition in regard to profitability, its general resource and fund management (2016, cited in Fatihudin et al., 2018, p. 554). This is linked to economic benefits such as cost reductions and increased efficiency, since firms who make good profit in terms of high revenue and lower costs generally have a high financial performance (Fatihudin et al., 2018, p. 554). However, AI is just recently experiencing a boom and might therefore be a risky investment. Thus, this study will look into the three measurements of financial performance; ROA, stock returns and volatility. This is in order to get a good overview of both internal performance, stock market performance and risk measured by volatility. As of now, there is a research gap whether the implementation of AI has indeed practically contributed to financial performance in Nordic companies during recent years.
1.3 Research Question
Does the implementation of artificial intelligence have a positive relationship on financial performance in Nordic companies?
1.4 Research Purpose
This study has the purpose to investigate whether implementation of AI in Nordic companies is contributing to financial performance. According to previous researches in the area, AI can have economical benefits in different ways throughout most industries, and the use of AI is predicted to grow in the future. As explained previously, Nordic countries have been found to be in the forefront regarding implementing technology in business operations. This laid ground to the interest in exploring whether the use of AI increases the probability of financial performance Nasdaq OMX Nordic. The technological Nordic market climate alongside with previous studies in the area of AI has generated the hypothesis that AI has a positive relationship with financial performance. Moreover, investments in new technologies might be risky, which is why it is of interest to explore both the internal, external and risk perspectives of financial performance.
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Regardless of the outcomes of the results this study will bring, the purpose is to contribute with updated knowledge within the area of AI and whether AI has created value to Nordic companies during the past five years. This relationship will be analyzed from both the shareholder and the stakeholder perspective.
1.5 Contributions
1.5.1 Theoretical Contributions
This study will contribute to the field of finance in terms of financial performance and AI. It will bring a better understanding of the relationship between AI and financial performance in the Nordics. It will also explore the connection of AI in regard to the disruptive theory, the dynamic capabilities theory, as well as the resource-based theory in terms of getting a competitive advantage of adopting AI. As previously seen, AI is shown to have economical benefits for companies utilizing the technology. However, there is currently a theoretical gap whether AI increases financial performance in terms of both return on assets (ROA), stock return and risk in the Nordics. Additionally, the study will investigate and further develop the opposing stakeholder and shareholder views in the field of AI. Lastly, the control variables; size, industry and country will provide additional theoretical insights of financial performance.
1.5.2 Practical Contributions
This study is going to provide practical contributions to all stakeholders of Nordic companies in terms of knowledge about financial performance and AI. For example, it will aid investors and help them in their investment process, deciding whether investing in a company that uses AI is a good idea, both in terms of internal performance, market performance and risk. This will as well aid the Nordic companies to better understand the financial effects of AI in their respective industry in order to have an edge over competitors. Additionally, since this study is examining the relationship of AI and financial performance in terms of the shareholder and stakeholder views, it will help the decision making of companies regarding whether AI implementation has a positive economic contribution. It will as well give an insight on the societal effects AI implementation might bring. If the results show that AI contributes to financial performance, it might encourage companies to invest in AI technology. Depending on one’s perspective, this may or may not have a negative impact on stakeholders in terms of increased unemployment and the negative social effects it brings. However, if the results turn out the other way than expected it might cause companies to invest less in AI. This could negatively affect stakeholders due to a decrease of the positive effects of AI contributes with in sectors such as the healthcare industry, as well as the convenience it brings. However, it must also be considered that AI is only booming as of recently, and the long-term effects of AI are still to be seen. Therefore, the study might also give an insight of whether AI tends to be a suitable investment for long respective short investment horizons. Additionally, this research will also show which stage Nordic companies are currently in regarding AI. For example, if they have just started using the technology, or if it is something that has been used for a longer time.
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1.6 Delimitations
This study is focusing on the Nordic market, however due to Norwegian companies being absent on Nasdaq OMX Nordic, only Sweden, Finland, Denmark, and Iceland are included. Therefore, this study cannot draw conclusions of AI and financial performance on the Norwegian market. However due to these countries sharing many similarities, it is deemed possible to still make generalizations of the Nordic market as a whole. Additionally, since this study is only conducted on companies listed on Nasdaq OMX, other large Nordic companies are excluded in the study. Furthermore, the companies on Nasdaq OMX Nordic are generally large public corporations, which results in smaller, as well as private corporations being excluded from the conclusions of this study. The study will investigate both companies that have and have not implemented AI, and a limitation is that there is no gradual analysis of the investments that have been made in AI as well as its contribution to the company. For instance, some companies in the sample might have invested less in AI but they are still treated the same in the regression analysis as the other companies. Furthermore, some companies might have a long process of research & development (R&D) in AI, but since it is not yet included in their business operations they are treated as a company without AI.
Financial performance is measured with ROA, stock return and standard deviation (SD) of risk, which means that it will not tell the relationship between the implementation of AI and other financial performance indicators. The focus is on AI, meaning that other researched technologies are excluded from the study. During the data collection process, the variable research and development (R&D) was excluded since there was not enough data available in the Thomson Reuters Eikon database. Moreover, data for 11 companies were not available in the database and were therefore excluded. Lastly, due to limited statistical knowledge of the authors and the fact that this master thesis is conducted in business administration, only adequate statistical tools were used. It is likely that more advanced statistics could have provided more detailed results.
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2. Theoretical Framework
This chapter will present literature regarding technology and AI in particular, as well as explaining how AI can provide economic benefits in various industries. The chapter starts by presenting the historical development about technology. This is in order to give the reader an understanding of how these technologies has contributed to change societies as well has it may impact the future. Thereafter, the focus will change to relevant theories regarding value creation and competitive advantage will be discussed in relation to the topic. Afterwards, the concept of financial performance will be presented, showing the link between economic benefits and financial performance indicators.
2.1 The History and the Future Prospects of Technology
Technological innovation has affected and revolutionized societies in different phases over time, which has contributed to economic growth and welfare. One historical invention of great importance was the “spinning jenny” back in 1768, which thought its efficiency could replace eight workers. This had controversially entailed downsides however, as for example reduction of the number of workers needed. An effect of this was an increased unemployment rate, which has been an ordinary outcome of innovations over time. Both the technical-progresses and the outcomes of innovations have resulted in changes, as new societal structures, methods of operations and living structures in general (Saint-Paul, 2008, p. 7-8). When it comes to the digital innovation era, the starting point can be traced back to two groundbreaking events. The invention of the transistor that was found by William Shockley and his co-workers in 1947, and the information theory by the pioneer of the area, Claude Shannon in 1948. Ever since these events, digital innovation has been systematic and has continuously changed societies. The integration of digital logic circuits was the starting point of the development of the microprocessors and the computer. Thereafter, telephones and the internet along with many other innovations has contributed to the analogical, digital omnipresent reality and drastically transformed the world within the recent couple of decades. The most recent tech- innovations that have revolutionized the digital business are artificial intelligence (AI), machine learning (ML), big data analytics, clouding and social media. All of these innovations are present in the everyday life of the modern society (Tekic and Koroteev, 2019, p. 685).
Technology does not completely replace the physical, tangible items but it contributes to a process where these items can be improved, complemented, and digitized (Iansiti & Lakhani, 2014, cited in Tekic and Koroteev, 2019, p. 685). Case (2017) explains in his book how the wave of new digital solutions is moving from infrastructure to highly regulated industries such as healthcare, energy, transportation, and finance. Today, digital solutions are widespread and all kinds of organizations in all sectors are advancing in these technologies in order to collect knowledge and information. This brings competence and strengthens the ability of the organizations to implement future business solutions (Tekic and Koroteev, 2019, p. 683). These new digital solutions are constantly changing the competitive field in business and are contributing with new ways of creating value (Tekic and Koroteev, 2019, p. 684).
The strongest drivers of economic growth have historically been capital investments and labor workforce. These factors have lost the effect recently in most developed countries
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and economic growth has seen a stagnation (Purdy & Daugherty, 2016, p. 3). Total factor productivity (TFP) has traditionally been a measurement of the growth created by the stock of capital and labor increase, or the efficient use of the two components. Growth of new technologies has been measured in TFP by economists and has worked as a measurement for the great technological breakthrough of the last centuries. This includes for example electricity, the railways, the telegraph, the steamship, and IT. The logic behind the recent stagnation is that the new innovations of the last decades cannot be repeated and bring revolutionary contributions to the economy (Purdy & Daugherty, 2016, p. 4-5).
However, now economies are entering a new take off of technology and transformative innovation, where AI is making it possible to overcome the physical limitations of capital and labor workforce, creating new possibilities of creating value and expansion (Purdy & Daugherty, 2016, p. 3). This new technology is not working as another driver of TFP, rather as a new factor in the form of a capital-labor hybrid. By performing and learning labor activities faster and better, AI can make tasks more effective, as well as completely replacing the need of manual work by the use of robots and intelligent machines. Furthermore, unlike conventional machines, AI can even improve itself over time due to the capabilities of self-learning, which will be explained more deeply in the next section. These effective contributions have been seen in for example the United States, illustrated in figure 1. This figure shows the normal process of business when AI is measured as a TFP (where there is a limited impact of growth) and where AI is used as a factor of production. From the figure it can be seen that the third alternative has a transformative effect on growth, and this ability of AI has a large capacity and potential (Purdy & Daugherty, 2016, p. 5).
Figure 1. Growth Scenarios For the U.S Economy
Source: (Purdy & Daugherty, 2016, p. 5)
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The fourth industrial revolution is initiated, and it has received increased attention from the business and research associations. It is important for companies to understand and learn the features and implications of the potential transformation, as for instance how the traditional machine dominant procedures are changing towards digital solutions. Predictions of the effects of the fourth industrial revolution will have a huge impact on social life. For example, it is estimated that robots will be predominant throughout all industries. This includes processes such as manufacturing, decision-making, problem solving as well as cooperating and coordinating machines. In general, digital solutions such as smart cities, big data analysis and similar applications will be commonly involved in society and are something that companies need to change and adapt for in order to stay competitive (Oztemel & Gurzev, 2018, p. 127).
2.1.1 The History and the Future Prospects of AI
The concept of AI has existed for more than half a century and was first formulated by John McCarthy in 1956. However, it was not until the recent couple of decades that technology improved to such a degree that it can fulfill the idea of developing AI technology. Furthermore, the technology is estimated to grow exponentially in the near future. In order to give a perspective, a computer built today has a computing power of approximately 1 billion times more than one built in the 70’s (Bini, 2018, p. 2359). This development is further illustrated in figure 2 below, where it can be seen that AI has seen a boom in the recent decade due to the advancements in deep learning.
Figure 2. Artificial Intelligence and its Subgroups.
Source: (Copeland, 2016)
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As can be seen in figure 2, AI has two subgroups, namely machine learning and deep learning. AI as a general concept was merely a dream back in the 50’s, with the pioneers having the idea of programming machines that could act just as humans (Copeland, 2016). The earliest form of AI was a program for the game “Checkers”. It worked by programming every possible choice of action into the algorithm to let the software choose the best course of action available for every move (Bini, 2018, p. 2359). This program could in its most basic form act as a human playing a game of Checkers. However, this is far from the AI that we think of today, which stems from the subgroup of AI called machine learning. What makes machine learning special is that the algorithm learns from past experience and can be improved by itself from simply learning by doing. It works by giving the software an input of data as a means of teaching it about something specific, for example which specific attributes that belong to a specific species of the eyes’ iris. By utilizing machine learning, the software is then able to distinguish these attributes accordingly. However, due to the process of learning by doing, the program is also able to categorize a sudden input of an iris that it has never seen before (Bini, 2018, p. 2359). The software learns to make predictions about the world it is experiencing due to past experiences (Copeland, 2016). Simon et al. (2018, p. 22) further explains how machine learning can perform two different types of tasks. The first one is the so called “Supervised machine learning”, which is exemplified in the previous mentioned scenario the machines ability to practice on a set of examples and later being able to accurately analyze new data. Secondly, there is the “Unsupervised machine learning”, which differs from the first example since this task is unsupervised, and thus excluding learning examples. The software therefore finds patterns in the inputted data by itself, as well as relationships between the different inputs (Simon et al., 2015, p. 22).
However, research and development in the recent past decade has led us to the concept of deep learning, which is a type of machine learning as seen in figure 2. Deep learning is rooted in the concept of an artificial neural network (ANN) (Simon et al., 2015, p. 23). It is therefore important to first grasp the concept of ANNs in order to fully understand deep learning. ANNs are toolsets, and systems which are influenced by how the human brain works by emulating the neurons of a real brain (Grossi & Buscema, 2007, p. 1046 ; Simon et al., 2015, p. 23). They are particularly useful since they are able to solve complex nonlinear problems by coming up with the best possible solution (Grossi & Buscema, 2007, p. 1046). Deep learning is a method of machine learning to train and model complex multi-layered ANN (Simon et al., 2015, p. 23 ; Benuwa et al., 2016, p. 124). Furthermore, it is due to the advancements in graphics processing units (GPUs), that deep learning has become a possibility (Simon et al., 2015, p. 23 ; Huang, 2016). Especially considering how much faster they are than conventional central processing units (CPUs) (Huang, 2016), The development of AI is going to further improve as GPU manufacturers such as Nvidia pushes the boundaries and develops new GPUs (Nvidia, n.d ; Shead, 2016). In general, the future's looking bright in regard to AI. This is because deep learning opens up endless possibilities due to its ability to break down complex tasks (Copeland, 2016).
Purdy & Daughtery (2016, p. 10) lists three capabilities of AI that simplifies it and provides a general understanding about its applications. Firstly, AI has a capability of sensing what is going on in the surrounding environment through the processing of audio and vision. A real-world example of how this is applied is for example in facial recognition applications. Secondly, AI is able to comprehend language and text through what is called natural language processing (NPL) (Purdy & Daughtery, 2016, p. 10). NPL utilizes machine learning, as well as computational linguistics (Jain et al., 2018, p. 161).
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It is very useful because it enables humans to interact with machines via text and language, which is processed by the relevant algorithm (Jain et al., 2018, p. 161). Furthermore, there are several different algorithms that are being used, depending on whether the input is in text form or by speech (Jain et al., 2018, p. 163). A common example of how NPL is widely used are in voice assistants, such as Apple’s Siri and Amazon’s Alexa, which can perform tasks after receiving an input of information either by voice or text (SAS, n.d). One can therefore imagine the endless potential of NPL and how it can be used to improve efficiency and everyday life of people. For example, one area of future research within NPL and deep learning is multiple modalities, which for instance can help capture other signals that are used in conjunction with language in the real world (Baltrusaitis et al., 2016, p. 2 ; Deng, 2016, p. 10). Lastly, AI has the capability of acting (Purdy & Daughtery, 2016, p. 10). This is commonly seen in autopilot systems, as well as the self-driving cars that have gotten much attention recently (Purdy & Daughtery, 2016, p. 10 ; Rudas & Haidegger, 2018, p. 108). The AI software is able to act independently without human control. Benefit of this AI capability in relation to self- driving cars is that it might decrease the amount of fatal car accidents caused by human control by 90% (Takács et al., 2018, p. 106). Furthermore, research has shown that self- driving cars lead to lower emissions thanks to performing optimal planning of the route in advance (Takács et al., 2018, p. 106).
PWC, which is a multinational corporation, defines AI by four different terms depending on the purpose of its application. These are: assisted intelligence, augmented intelligence, autonomous intelligence, and automation. Assisted and augmented AI work synergistically with humans. However, the former is non adaptive and does not adapt from the interactions made with the human counterpart as augmented intelligence does. Henceforth, while assisted intelligence is AI used to make simple tasks faster to perform, augmented intelligence is the definition of AI used for example in the optimization of decision making (Verweij et al., 2017, p. 2). On the other hand, automation and autonomous intelligence are AI forms that do not require a human in order to function. Automation of tasks is when a machine performs otherwise manual labor, and thus eliminates the need of a human worker. Autonomous intelligence however is a bit more complex. Similar to augmented intelligence, it aids in decision making. However, no human interference is needed in the process (Verweij et al., 2017, p. 2).
To conclude, AI is a broad topic with several different definitions. However, as a technology it certainly has many uses in regard to efficiently perform complex tasks. Furthermore, there is huge potential within the future of AI and prospects are looking very promising due to the advancements in computing power and the endless possibilities that have opened up due to deep learning.
2.2 Challenges of AI
2.2.1 Cyber Risks
New technology entails many opportunities for stakeholders, which can contribute to social progress leading to economic growth. However, these potentials also have to be outweighed by the associated risks. Some of the negative sides of AI is that it can be used for misaligned incentives, adversarial geopolitics, or malicious intent. For instance, in cases when the technology is used for email scams or cyber-welfare (Floridi et al, 2018, p. 291). Other risks can be manipulation of simulated markets driven by AI, a unifying
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word for these criminal risks is AI-crimes (AIC) (King et al. 2018, p. 89). AI also has risks in the form of societal impacts, and can both contribute to human flourishing as well as decreasing it, which raises ethical questions about the societal impacts. There is a risk that the true potential of AI not being used due to the risk of overuse or misuse (Floridi et al, 2018, p. 291).
New technologies lead to new risks, one example is the cyber weapons, which is something that has to be considered and counteracted. Cyber weapons pose huge risks, since they are easy to use with a low entry cost and high chances for success in asserting authority. Furthermore, cyber risks can jeopardize stability and security in societies. The new risks and the conflicts it bring differs from the traditional and violent ones (Taddeo, 2018, p. 339). Governmental and non-governmental actors, policy authorities and military strategies have therefore stressed the need of developing cyber intimidation as a new strategy to prevent international stability (European Union, 2014; International Security Advisory Board 2014; UN Institute for Disarmament Research 2014; UK Government 2014; European Union 2015).
AIC and the related research are scattered across many fields such as; socio-legal, computer science, psychology and robotics among many others. Since AI is relatively new, there is a lack of studies and solutions regarding potential criminal activity (King et al. 2018, p. 90). King et al. (2018 p. 90) is also providing a literature analysis of the area of AIC. In the article, evidence of AIC's possible role of conducting crime is provided in the form of two research experiments. One experiment was performed in social media by Seymour & Tully (2016). It was conducted by sending out a mass of messages, convincing people to click on a link where each unique link collected information about each user's previous activity and personalized information, hiding the real intention behind the message. This was information that could be used for fraud and illegal activities. Another experiment was performed by Martínez-Miranda et al. (2016). It was found that trading agents can execute a manipulated campaign that is profile-based, consisting of false orders. These are some experimental examples of how AIC can be conducted. In a study by King et al. (2018 p. 113) possible risk areas where AIC can be conducted is presented, concluding that extended knowledge and further recognition of some areas is required in order to get a better understanding. The mentioned areas where; AI in interrogation, social engineering attacks, homicide and terrorism, weaponized drones, and self-driving vehicles. The researchers were also collecting information of areas where the technology could be dual used, which means that there is a risk that tools created for legitimate use may be used for crimes. The higher the development of AI reaches, the higher is the risk of malicious or illegal usage, which may lead to regulation leading to delimitations of how AI can be used. How AI can be used to prevent criminal acts and security, is also brought up by King et al. (2018, p. 114). The most relevant starting point in the cybersecurity field, demonstrating an offensive-defensive approach, making it possible for AI systems to identify and strengthen their own vulnerabilities, was presented in the 2016 DARPA Cyber Grand Challenge (King et al. 2018, p. 114). Regarding the area of cybersecurity, IBM released cognitive SOC in 2018, which is an application of machine learning algorithms using structured and unstructured data of organizations. Laws are playing a key role in mitigating threats of dual users of technological development. However, the cognitive SOC is also used in the process of work through data around security information and risks with the equivalent aim of improving recognition, responses and reduction (King et al. 2018, p. 114-115).
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There is a lack in the research of individual factors behind the criminal act of programmers, developers, and users of AI for AIC, as well for the victims, in order to counteract AIC. AI may play a big role in organized crimes, areas such as well-organized drug cartels. Many criminal organizations are acting on the dark web, which is what Europol is referring to as crime-as-a-service, when the purchase is between seller and buyer. This act can be driven by AI even more in the future, for instance by profile hacking (Europol, 2017). It is important to develop an understanding of areas where AIC exists as well as where it can arise, as a way of preventing and implementing redressing policies (King et al. 2018, p. 115).
2.2.2 Societal Impacts of AI
Floridi et al. (2018) are providing an explanatory framework of the societal impact of AI. One of the areas brought up is what humans can become with AI as an aid. AI is creating solutions which can work as aid for humans, cut costs and create efficiency, which enables individuals to reach self-realization. People get more opportunities to focus on their own characteristics, interests, goals, and potentials which are positive impacts. However, it can also contribute with downsides since it can absorb attention from other important areas. This can lead to a devaluation of other competences and a disruption in the labor market. The employment circumstances can be affected on both an individual and social level. At an individual level, occupation can be linked to personal identity, social roles, and self-satisfaction. These influences may be adversely affected by redundancy and can in the end lead to economic harm. To connect this to a societal aspect, it can harm skill-intensive areas as for instance in health care diagnosis, aviation, or other vulnerable situations where malfunctioning of AI and lack of human competence would cause terrible outcomes. It is important to foster the development of AI simultaneously as foreseeing potential risks and mitigate its impacts by closely studying radical innovations. A solution is to find an integrated solidarity between the present and future aids to ensure that a disruptive transition can be completed in the best way (Floridi et al., 2018, p. 691).
Another area presented by Flordi et al. (2018, p. 692), is what can be done with AI. The use of AI as a “human agency” is growing and can have great improvements for the society. Productivity can be increased, getting smarter, better, and faster thanks to the backing on AI. Responsibility is essential in the development of AI. For example, what type it is, how it is used, who and in which areas advantages and disadvantages will affect. There is a risk when AI systems used for decision-making are beyond human understanding and when there is an absence of human responsibility. Examples could be high-profile cases as accidents in autonomous vehicles as well in more commonly automated creditworthiness that goes wrong. The weight of how much the degree of agency should be delegated among people and autonomous systems has not been ideally balanced yet, neither ethically nor pragmatically. If the AI system is designed and used effectively, it can work as a supportive function to the human agency and have a likelihood of good outcomes and strengthen morality (Floridi et al, 2018, p. 292-293).
What can be achieved collectively with AI is another part of the analysis by Flordi et al. (2018, p. 293). AI can be used to support human coordination leading to more ambitious goals. For example, a better spread of resources can lead to a sustainable consumption and henceforth a more sustainable approach. In this area, there is also a risk of giving too much of the responsibility to AI, resulting in humans not being in the loop of what is
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going on and thus losing control. AI can also support human interaction. In order to cooperate with complex coordinates around the world, stakeholders have to work together. AI could by an algorithmic system create societal coherence. This kind of system could for instance be useful as a societal framework encouraging taking environmentally friendly actions. This is an example of a system that humans or societies who are self-nudging to take environmental responsibilities could use. It would require them to take their own actions, but the AI would be the solution in order to implement and facilitate. The risk in this area is that AI-systems can affect and decrease self- determination of humans since it may lead to changes in behavior. Using AI is meant to accommodate human routines and the automation will make lives easier, be at the service of self-determination of people and increase societal coherence. Even if not intentionally, there is a risk that the AI services lead to that humans do not fulfill their potential greatness which can lead to undermining of human flourishing (Floridi et al, 2018, p. 293- 294).
There are however physiological downsides of AI in terms of its effect on humans. One perspective discussed by Turchin and Denkenberger (2018, p. 153) is the risk that human addictions to AI technologies go too far. Such addictions can be widespread and damage “normal” human life. Game addiction changed kinds of social networks, virtual reality, fembots and designer drugs can for instance lead to lower fertility and a shorter life expectancy. This is already happening to some degree in Japan in the Hikikomori generation, where social withdrawal is common, where people are spending time with technology instead of humans. One demographic consequence of this is that people are not forming as many families (Saito & Angles, 2013, cited in Turchin & Denkenberger, 2018, p. 153-154).
2.2.3 AI´s Impact on the Workforce
It is to no surprise that people are afraid by the risk of losing their job due to the advancements within AI. On one side, AI will be able to work synergistically with humans and thus increasing their productivity. One the other side however, it can also replace workers with different tasks in a vast variety of sectors (Frank et al., 2019, p. 6531). However, whether R&D of AI is positive or negative for employees depends on one’s perspective on the subject. Frank et al. (2019, p. 6531) talks about three opposing perspectives regarding AI’s impact on the workplace. The worst-case scenarios are realized for those having a “Doomsayer’s perspective”, which means that AI will make human workers become obsolete. This is because they can easily be replaced by a cheaper and more efficient machine. Furthermore, studies have indicated that 59% of the German workers are at risk of losing their job due to automation of work tasks. Representing a Nordic country, Finland also risks high unemployment rates, albeit lower at 35% (Frank et al., 2019, p. 6531).
On the more positive side, there are the “Optimist’s perspective” and the “Unifying perspective”. The Optimist’s perspective emphasizes that while the technology might replace some workers, the advantages from cost reduction compensate for it. Furthermore, the optimists believe that new jobs, requiring tasks that are difficult for a machine to accomplish will arise, for example social related skill sets. It might also create a need for new working tasks or businesses (Frank et al., 2019, p. 6531). Similar scenarios have occurred in the past, for example when the development of the car led to new businesses such as diners and gas stations opening up as a result of the new technology.
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Lastly, a unifying perspective means that technical developments directly impact different skill sets belonging to different lines of work, and thus do not wholly impact an entire occupation (Frank et al., 2019, p. 6531). This could be interpreted as that workers would keep their jobs, but certain tasks might be replaced by a machine, or made more efficient through machine and human collaboration. Jarrahi (2018, p. 9), also argues for the possibility of a partnership between AI and humans in regard to decision making. It is noted that while AI can perform complex analytical tasks, humans are still useful for tackling uncertainty which requires a more creative and spontaneous way of handling (Jarrahi, 2018, p. 9).
Even though AI will replace human labor to some extent, there will still be a need for human skill. Job demographics will shift from the historical models, however, creativity, interpersonal skills, emotional intelligence, and problem solving are not simply replaceable by AI. In for instance the financial sector, there are some predictions that human advisors will completely be replaced by AI. On the other hand, there are also predictions that AI will still be used in conjunction with the advisors, whilst still replacing highly standardized jobs (Wheeler, 2020, p. 69). According to a survey of 352 scientists from Yale University and Oxfords university, the human workforce will not be replaced completely before at least another 100-200 years (He et al. 2018, cited in Wheeler, p. 69, 2020). The fact that AI is changing the demographics on the labor market creates many challenges to society. New kinds of jobs will emerge, and employees will have to be open to learn new skills which will clearly include technology competence, insight and creativity. This transition of skills is seen as one of the largest challenges in the implementation of AI, which creates a large importance of organization management. Many organizations have already started the implementation of AI, and the early adopters will get a lot of advantages of efficiency and might be the market leaders. A mix of the most developed technology in combination with talent will be predominant in order for organizations to continue flourishing (Wheeler, p. 69-70, 2020). Furthermore, robots taking over jobs can lead to harmful social impacts, for instance there is a risk of people losing their meaning and self-worth as a consequence. For this reason and many others, it is important that considerations about this are taken along the way (Turchin and Denkenberger, 2018, p. 153).
2.2.4 Operational Costs of Implementing New Technologies
Applying AI in a company does come with practical challenges to both the operational and functional structures. These challenges include transformations in the core businesses, reconstructed management approaches, new business strategies and changes in the whole organization. These are important issues which managers have to deal with. Matt et al. (2015, p. 341) presents perspectives on digital transformational strategies in firms. Implementations of new technology focuses on the management of IT infrastructures throughout the whole firm. This matter can to some extent limit the focus on product innovation on customer-focused opportunities which come along with new technologies.
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Figure 3. Relation Between Digital Transformation Strategy and Corporate Strategies
Source: (Matt et al. 2015, p. 342).
Tekic and Koroteev (2019) is also researching the topic by overlooking the existing framework of strategies within the field. This includes two dimensions: digital technology usage and business models of operational strategies (Tekic and Koroteev, 2019, p. 686- 687). The technology is used in different dimensions depending on the business and defining a strategy might thus be a complex issue. There is a broad area of managerial perspectives that is valid on the implementation of new technology. For instance, the aim of certain companies is to adopt new technologies, while for others the aim is to use technology in order to interact with customers and get feedback (e.g. social media). For some companies, the technology is a new innovation and a completely new way of doing business. Digital transformation can be used to explore new areas and create value, but also improving and transforming existing ones, increasing efficiency, and cutting cost. Companies' objectives of applying new technology is both to reach success and to survive (Tekic and Koroteev, 2019, p. 684). In the same study a typology was created, which can serve managers with new insights in the area and help them to choose an approach more systematic and heuristic. Factors such as; motivation, aim of transformation, management style, employee skills (as entrepreneurial spirit and creativity), are all risks in the process which can be consequences in the case of a failure (Tekic and Koroteev, 2019, p. 691- 692). Since these challenges apply to all kinds of digital transformations, it is also a challenge that has to be considered for companies in the process adopting AI.
2.3 Artificial Intelligence in Different Industries
The benefits of AI are evident in Accenture’s study on the effects of AI on different businesses. The study included 12 developed countries (Purdy & Daugherty, 2016, p. 3), including Sweden and Finland as representatives of the Nordics (Purdy & Daugherty, 2016, p. 8). The study showed that AI is likely to increase workers’ productivity up to almost 37% by the year 2035 (Purdy & Daugherty, 2016, p. 3), as well as doubling the economic growth rates (Purdy & Daugherty, 2016, p. 3). Sweden and Finland showed the highest potential of increased labor productivity due to AI, with figures amounting to 37% and 36% respectively (Purdy & Daugherty, 2016, p. 17). These studies are some examples of how AI can create efficiency. In order to grasp a deeper understanding of how AI has contributed with practical benefits and improvements which can be linked to increased financial performance, AI usage and the contributions in different sectors will
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be presented. The most common industries where AI is implemented will be presented below.
2.3.1 Food Industry
In the food industry, one study was made for analyzing the benefits of using AI in the food development process. A questionnaire was handed out to 250 different food producing companies in Iran. The study showed that AI can be used as a tool for the executives in the decision making of whether a new food product should be launched or not. This is particularly useful due to the ability of avoiding unnecessary costs due to product failure (Soltani-Fesaghandis & Pooya, 2018, p. 848). The argument for the usefulness of AI is further strengthened by the fact that almost 90% of new food products fail within the first year of being launched (Soltani-Fesaghandis & Pooya, 2018, p. 848). Because of the ability to avoid costs related to failure, it can be argued that AI can be used in order to increase the overall performance of companies within the food industry.
2.3.2 Healthcare Industry
New technology and solutions brought to the healthcare industry in the form of AI will change how health conditions can be prevented, diagnosed, and cured. These solutions are often driven by entrepreneurs and startups. AI is applied to clinical, financial, or operational health care areas. There are many beliefs that the future of how information is collected as well as interactions with patients, families and staff will be revolutionized by AI. Today there is already an existing AI application that is relevant and tasks that previously required face-to-face interactions have moved to centralized platforms, some driven by chatbots and other virtual assistants. (Garbuio and Lin, 2019, p. 59). This is an example of the natural language processing (NPL) that has been explained previously. In healthcare the technology of AI has contributed to lowering costs, accelerated drug discovering and improving outcomes. In recent years, attention has been paid to these improvements in healthcare as well as an increase of venture capital to the sector (Garbuio and Lin, p. 61. 2019). Furthermore, PWC believes in the benefit of using AI in the healthcare industry in order to identify possible pandemics (Verweij et al., 2017, p. 14). This is interesting, considering the current Covid-19 pandemic that is taking place during the writing of this thesis. Additionally, AI provides diagnosis more efficiently and saves time, which eventually means that the consumers are getting treated better and thus saves lives (Verweij et al., 2017, p. 14).
One example of a startup that utilizes AI in the healthcare industry is Aindra, which is using an AI platform utilizing medical image classification, to efficiently and correctly address diagnosis of cancer. Augmented intelligence in the healthcare context can be explained as using AI in order to perform things that could not be made without technology. This is done through sophisticated algorithms that are created for NPL and is deviating for a large amount of accumulations of data and records. Algorithms in this area have more customized components, requiring more sources of data. These are commonly used in precision medicine (the potential in techniques to find the right treatment to the right patient) and using data to find patterns in epidemiologic data. The use of augmented intelligence may require a change of business model. One example of this is iCarbonX that uses an enourmous dataset and AI in order to provide individualized customer care. It is rare and a low number of startups using this kind of AI so far. Advanced AI technology and transparency of algorithms is required in order to build reliable systems.
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Mayo Clinic is an example of a company using AI, by working on creating a hospital without doctors. This is still in an early phase and is being tested to reach security standards. Some components are already existing although, for instance, surgeons are getting assistance from robots in the surgery rooms (Garbuio & Lin, 2019, p. 63). AI solutions such as systems driven by robot assistants, reveal medical personnel and applications that more efficiently and successfully are preventing diseases and saving lives are improving healthcare in many important ways. These implications are creating value and higher efficiency are leading to economic benefits.
2.3.3 Automotive Industry
One industry that is facing major changes due to AI is the automotive industry. Approximately 10% of automotive corporations had already invested in this technology in 2018 (Marr, 2019a), and it might be beneficial for corporations to prepare and adapt to this transition in order to survive a transformation of the industry.
There are numerous benefits of self-driving cars that utilize AI. Research has shown that automated driving would decrease the amount of American road accidents with a staggering number of 90% (Soegoto et al., 2019, p. 5). The reason for the increased safety of autonomous driving comes from the benefits of highly advanced sensors that provide accurate measurements of the driving environment. This in turn aids the AI in making accurate driving maneuvers without hesitation, which removes many risks related to human driving (Cunneen et al., 2019, p. 2). In addition, this type of sensor would also contribute to less traffic, as a result of cars being able to have a closer distance to each other on the road. This mitigates side effects of traffic congestion, such as increased blood pressure and depression (Soegoto et al., 2019, p. 5). Adding to the benefits of road efficiency and safety, autonomous driving also provides comfort to the passengers. Instead of focusing on driving, the car users will have an increase of free time to spend on other activities (Soegoto et al., 2019, p. 5). Realistically, this could include activities such as reading newspapers, watching videos, or using the phone which otherwise while driving would be a safety risk.
It is not only car manufacturers that are affected by AI and self-driving cars. The ride- hailing company Uber sees this new technology as a key to achieving profitability in the future. By launching autonomous cars, Uber’s strategy is to cut costs related to human driving and ultimately increase profitability. If proven successful, this would be game changing for the company, since the driver costs currently amount to 80% of the services cost (Shetty, 2020). Predictions have also been made that car-hailing might decrease the need of purchasing a car by 80% in 2030 (Garfield, 2017), which further strengthens the financial outlook for companies such as Uber. However, some autonomous cars have caused struggles in the past resulting in mixed opinion in the eyes of the public. A famous example is when a self-driving Uber car was involved in a fatal accident in Tempe, Arizona (Shetty, 2020). Although investigators believe that the crash was mostly due to lack of awareness of the human operating the car (Lee, 2019), it provides doubts of the current comfort level and need of human intervention while on the road. Priceless contributions in creating a safer and better environment for humans as well as time savings and increased efficiency by automation processes are all factors that are leading to economic benefits to companies and societies.
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2.3.4 Financial Industry
When coming to the financial industry, AI is seen as the most disruptive technological development. Accenture conducted a survey regarding the outlook of AI between the years 2018 to 2022, and it was issued to workers and senior executives in a variety of industries and countries (Shook et al., 2018, p. 6). The analysis indicated that banks who invest in AI and put emphasis on the collaboration between regular workers and machine learning technology, could increase their revenue of around 34 percent (Shook et al., 2018, p. 3). This assumes however that the rate of AI implementation matches that of top performing corporations (Shook et al., 2018, p. 3). The survey also indicated that banks could increase their employment rate by 14 percent in 2022 (Shook et al., 2018, p. 3). Overall, this should mean that AI implementation is beneficial for both the shareholders, as well as the stakeholders of the banks.
Furthermore, Wheeler (2020) is providing information of how the financial market will be transformed by AI. Day to day operations of organizations have to adapt to these new technologies in order to stay competitive. AI makes it possible to access and analyze events and news of a huge amount of financial data in a way that is not possible for humans. The competitiveness in the financial services landscape is increased by the development of AI. The new entrants entering the market landscape such as PayPal, Amazon and Venmo are changing the rules and are forcing existing financial institutions into new products, solutions and services through new development and accessibility. AI provides customers with personalized investment strategies and wealth management techniques, as well as contributing with robo-advisors and chatbots. This creates customized solutions and services to clients, which is changing how customers are seeking for help (Wheeler, 2020, p. 66).
It is predicted that by 2030, insurance companies, investment firms and especially banks, can save $1 trillion USD by implementing AI technologies into their business models. The possibility of online assessment and help of chatbots, who are always available outside of traditional working hours, has contributed to increased flexibility. Therefore, physical bank offices have seen a decrease in recent years. New technological aids are appealing for millennial consumers and will continue to emerge. Traditional, underserved markets of smaller investors are no longer presumed as profitable to larger, established organizations. It is predicted that those working as investment advisors will experience a serious job loss by 2030. Additionally, other areas and jobs expected to be affected are securities, commodities and financial services, compliance personnel and personal financial advisors. Specifically, in the banking sector, the impacted jobs will be loan officers, customer service advisors, loan interviewers, tellers and compliance personnel (Wheeler, p. 67-68, 2020).
Furthermore, financial planning can be divided in two groups; mathematics and human behavior. AI can make estimations taking many kinds of aspects such as savings, spending, life expectancy and health patterns into account, providing financial suggestions and utilizing profits. In the future, AI is predicted to take emotions into account when providing recommendations. As for instance during volatile market conditions and other areas behavioral finance situations where emotions play a key role. Patil and Kulkarni (2019) were investigating the usage of AI and its adoption in customer chatbot advisors. Chatbots are built on the usage of NPL, providing the financial sector with service and financial investment advisory to customers. When comparing factors
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such as conveniences and costs, chatbots are seen as better than human advisors. A chatbot can examine a larger number of accounts than an advisor and have a lower operational expense. But regarding effectiveness and accountability, human advisories are preferred. Chatbots can provide customers with advice based on historical data, but cannot motivate customers to focus on their financial goals in the same way as a human advisor (Patil and Kulkarni, p. 4301, 2019). AI working as aids to humans or substituting standardized tasks, performing over human capacity and higher availability will all lead to increased efficiency in the financial sector and result in economic benefits.
2.3.5 Energy Industry
The potential of AI is also evident in the energy industry. A recent report has predicted that by 2024, the use of AI in energy markets will amount to $7.78 billion USD. This would equal to a compounded annual growth rate of 22.49% from 2019 (Banga, 2020). This indicates that the implementation of AI in the energy industry is growing and will be more evident in the future. Furthermore, the use of AI is financially beneficial for energy corporations. For example, by saving costs by predicting asset failures, as well as providing tools for more efficient decision making (Fehrenbacher, 2019).
Bassiliades & Chalkiadakis (2018, p. 1) are providing information about AI technology and its involvement in smart grids. This is predicted to be the future of energy generation due to its efficiency and emphasis on sustainability. Electricity is crucial in today’s society and it is vital to have access to energy sources that efficiently meet the demand (Ahat et al., 2013, p. 196). By boosting energy efficiency, firms and citizens will experience financial benefits. Moreover, effective energy generation emphasizes sustainability by reducing greenhouse gas emissions (Ahat et al., 2013, p. 196). This is where AI comes in, due to its uses in many areas for effectively implementing smart grid systems. Smart grids require complex problem-solving algorithms in order to effectively generate energy for customers with various energy demands (Ramchurn et al., 2012, p. 88). Another area of smart grids where AI can be used is cyber-security, since it has the potential of providing security solutions for example in regard to decision making in the events of a cyber threat (Bassiliades & Chalkiadakis, 2018, p. 2). Additionally, AI is also helpful for security assessments regarding unpredictable static and dynamic scenarios in the power system, as well as forecasting the demand for energy production (Ramos & Liu, 2011, p. 5). Optimizing energy usage by the use of smart grid systems creating lower impacts on the environment are leading to valuable benefits as well as economic benefits in the area of energy.
2.3.6 Supply Chain Management
Hartley and Sawaya (2019) conducted a study regarding how AI can improve the process of supply chain management. This was performed by 14 analyzing large and mature manufacturing companies and the way they adopt and implement digital processes. This included robotic process automation (RPA), artificial intelligence (AI), machine learning (ML) and blockchain technology. Some examples of how AI is used in supply chain is the use of automotive delivery vehicles, such as robots used to manage inventories by scanning store shelves and improving the packing to trucks (Wiles, 2019, cited in Hartley and Sawaya, 2019, p. 710) In the study it was found that most of the supply chain businesses investigated was using ML. These were applications such as weather data studies used to improve transportation management, rerouting vehicles in order to avoid
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congestion and risk management. Additionally, it was also used in forecasting the demand for scheduling equipment maintenance and warehouse pick-up processes (Hartley & Sawaya, 2019, p. 710). Furthermore, Bottani et al. (2018) studied the benefits of AI by investigating the use of AI in the wholesale industry. This study was done by applying two artificial neural networks (ANN), one used in deriving the selling prices offered by the wholesaler and the other for estimating the demand. The ANN was then tested through the wholesales supply chain. They found that using AI can decrease economic losses of out-of-stock situations by 56% (Bottani et al. p. 698, 2018).
In the freight company DHL, ML is used to predict flight delays and the underlying factors. This helps in order to know when to deliver and which airline to use (Gesing et al, 2018; Banker, 2019, cited in Hartley and Sawaya, 2019, p. 710). AI can create much value when it comes to supply chains, but currently it is not widely used in this regard. Many firms do not have enough expertise or knowledge when coming to the area of implementing AI in their supply chain processes. It is predicted that AI/ML will be a part of most companies' supply chain planning and execution in the future (Hartley and Sawaya, 2019, p. 710). However, the process of implementing it into organizations need to be planned and this transformation may still be years away. It is important to prepare, define opportunities as well as critical areas, educate people, research technologies and find the most suitable one in order to decrease technological transformation barriers (Hartley and Sawaya, 2019, p. 714). In the supply chain processes, AI can contribute in automatization of transports, robots performing inventory checks and demand forecasting leading to less waste, identifying, and predicting potential problems. These are all examples who are contributing to positive economic effects for companies successfully implementing these AI functions.
2.4 Financial Performance
The concept of financial performance is explained by IAI (2016, cited in Fatihudin et al., 2018, p. 554), as a firm’s financial condition in regard to profitability, as well as its general resource and fund management. Firms who make good profit in terms of high revenue and lower costs generally have a high financial performance (Fatihudin et al., 2018, p. 554). It is therefore assumed that companies who maintain economic benefits due to AI, such as lowering costs, should in theory achieve a higher financial performance. However, a firms' financial performance can be measured by different financial ratios derived from relationships between financial variables (Leach & Melicher, 2018, p. 164), which are published regularly in firms’ financial statements (Fatihudin et al., 2018, p. 554). These ratios include for example; Return on Investment (ROI), Return on Assets (ROA), Return on Equity (ROE), among others (Fatihudin et al., 2018, p. 555). This study looks into three measurements of financial performance, namely ROA, stock returns and standard deviation of stock returns. This is because they provide a good picture regarding both internal performances, as well as the market performance from an investors’ perspective, including risk levels.
2.4.1 Return on Assets (ROA)
ROA is a performance measure that indicates how efficiently a firm is using its assets. It is derived by dividing the net profit with total assets (Leach & Melicher, 2018, p. 64). The accounting firm Deloitte explains that companies with a high ROA are essentially doing well in terms of internal performance, since assets are then properly utilized in a
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way that generates value (Hagel et al., 2013, p. 7). In a study of industrial companies by Matar & Eneizan (2018, p. 7), it was concluded that ROA is positively related with the financial variables; revenue, profitability and liquidity. Additionally, ROA was shown to have a negative relation with leverage, meaning that highly leveraged firms are less likely to perform well financially in terms of ROA. These results indicate that if AI causes an increased profitability for companies in regard to cutting costs or getting more sales, it is likely resulting in an increased financial performance as measured by ROA. Furthermore, the process of analyzing a firm’s profitability through performance ratios such as ROA, is a way of getting an understanding of the financial development of firms as well as past and future achievements (Heikal et al., 2014, p. 102). This further explains the usefulness of using ROA to get a grasp on the internal financial performance of firms.
2.4.2 Stock Return
How well companies are doing financially can also be interpreted by following stock returns. Stock returns are fundamentally the return investors get from investing in stocks and are essentially derived in a percentage form by dividing the current stock price with the stock price at the time of investing. It is therefore similar to return on investments (ROI) as a performance indicator, except that ROI is derived from the revenues minus the amount invested, divided by the invested amount (Gilfoil & Jobs, 2016, p. 639). Companies who are performing well are more likely to see a positive impact on stock prices, which in the long-term leads to increased stock returns (Irfani, 2014, p. 105). Furthermore, in a study made by Jarrell & Darkey (1992, cited in Irfani, 2014, p. 105), a connection was found between stock returns and the financial performance indicators such as ROA, Earnings per share (EPS) and ROE. A company that increases its ROA would thus see a higher stock return, as well as increased financial performance relating to the discussion above. Additionally, stock return can also be predicted by financial indicator ratios that are used in relation to market prices. Examples include the price- earnings ratio and the price-to-book value ratio (Irfani, 2014, p. 105). Companies’ stock returns can therefore be an interesting performance measure from an investor’s perspective due to its market connection. Stock prices and the amount of return from stocks are of key importance for investors, since it is the basis of whether they will make money on an investment or not. OMX Nordic 40, which includes 40 large Nordic companies, had an annual return of 20.74% in the calendar year of 2019 (Nasdaq, n.d. a). However as of current, the year change is only 3.46% (Nasdaq, n.d. b), likely due to the Covid-19 situation.
2.4.3 Standard Deviation of Stock Returns
When talking about stock returns, it is important to understand the concept of random walks. It tells that stock prices move randomly and cannot be predicted from past information, thus making investors unable to beat the market unless additional risk is included (Chitenderu et al., 2014, p. 1243). The only way for investors to beat the market without undertaking risk is if there is information asymmetry, meaning that one party possesses more information than others (Chitenderu et al., 2014, p. 1246). Risk and uncertainty is commonly associated with volatility. This is because when something is volatile, it tends to fluctuate. Additionally, the more volatile something is, for example stock prices, the more they tend to change (Daly, 2008, p. 2378). Studies have also shown that excess returns from stocks have a positive relation to the stock returns’ expected volatility, and it has a negative relation to the unexpected volatility. When stock returns
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are lower than usual, investors speculate more which leads to an increased volatility on the market. However, it is also possible that the expected volatility positively impacts stock returns, due to investors demanding a higher compensation when taking on riskier investments (Chiang & Doong, 2001, p. 307). As previously explained, it might be risky to invest in AI due to it being relatively new and its long-term effects on society being unknown. However, if that is the case, companies investing in AI might also have a higher stock return, due to the higher risk and reward from undertaking such an investment. A frequently used measure of risk in terms of volatility on stock returns is the standard deviation (Daly, 2008, p. 2379). This makes the standard deviation of stock returns an efficient variable from a risk averse investor’s perspective, since it takes into account the underlying risks, such as the relationship between AI and its possible effect on stock price fluctuations.
2.5 The Stakeholder and Shareholder Theories
The two rivaling theories, shareholder theory and stakeholder theory, are commonly referred to regarding the corporate social responsibilities (CSR) of businesses and their management. The main difference between the two theories is that the former emphasizes that the goal of a business is to focus on maximizing shareholder value, while the latter focuses on creating value for all stakeholders (Rönnegard & Smith, 2013, p. 184). As seen before, AI implementation has both benefits and drawbacks for stakeholders. For this reason, these two theories are seen as important to analyze and discuss in relation to AI and financial performance.
2.5.1 The Shareholder Theory
The shareholder theory has its roots from the book “Capitalism and Freedom” (Rönnegard & Smith, 2013, p. 184), which was written by Milton Friedman in the early 1960’s. Friedman explains how the primary goal of any corporation should be to maximize the value for the shareholders. The view that companies have social responsibilities is a misconception of a free economy. However, maximizing shareholder value can simultaneously be a way of acting socially responsible in the way of promoting a free market economy, since investors can themselves decide where social interest lies (Friedman, 1962, p. 133). By spending shareholders’ funds on other activities than maximizing shareholder returns, it would lead to not following shareholders' interest on their own investments (Friedman, 1962, p. 112-114). What social responsibility is can be interpreted differently, which contributes to the motivation of maximizing shareholder wealth, increasing individual freedom and supporting free market economy (Friedman, 1970, p. 2). Friedman (1970, p. 2) refined his interpretation, stating that the responsibility of business managers is to act as employees to shareholders, making decisions of business operations upon their requests. Following the shareholder theory, business managers should spend capital in such a way that investors get the maximum amount of return from their investment. However, the value for stakeholders connected to the firms, such as the employees and consumers, are neglected, which is something the stakeholder theory is often criticized for (Tse, 2011, p. 52-53). The stakeholder theory and the reasoning of profit maximization has been questioned. Benefits that could possibly be gained from taking consumers, employees and other stakeholders into consideration are ignored and therefore areas where potential profit can be created are ignored. If these potentials were considered it could lead to more increased profitability than only focusing on profit maximization in operations (Magill et al., 2013, p. 37).
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2.5.2 The Stakeholder Theory
As opposed to the shareholder theory by Friedman, the stakeholder theory emphasizes that the right method of acting socially responsible is to consider value maximization for all stakeholders of a business (Freeman, 2010, p. 5). In addition to maximizing shareholder value, the stakeholder theory also considers all groups connected to the business and composes the whole society where the company operates, such as employees, customers, communities, and governments (Benn et al., 2016, p. 2). In some scenarios, conflicts might occur between the stakeholder groups. This should be addressed by trying to benefit as many stakeholder groups as possible, with the main goal of increasing the value even further for each group (Freeman, 2010, p. 5). The primary objective for corporate managers is to benefit all stakeholders, because it will conclusively lead to value maximization of the shareholder group (Freeman, 2008, p. 166). This means that the two theories are quite agreeable rather than contradicting each other (Freeman et al., 2010, p. 12). Freeman´s (2008) perspective is that the stakeholder theory can be viewed as comparable to good management, a corporation succeeding in all of these areas will achieve profit maximization (Freeman, 2008, p. 165). Focusing on the wealth of all stakeholders will lead to that company's undertaking social responsibility in its business operations without compromising either foundations of capitalistic market economy or the shareholder wealth (Mansell, 2013, p. 8). In Freemans book Strategic Management: A Stakeholder Approach, it is explained that stakeholders are affected by a corporation, but the success of a corporation can also be affected by the stakeholder to a large extent (1984, p. 25;55). Therefore, it is of high importance to prioritize all stakeholder needs and interest and involve these in their strategic management (Freeman, 1984, p. 43).
Stakeholders can be sorted in two categories which have different degrees of connections to a corporation (Freeman et al., 2010, p. 24). The primary stakeholders are the most important ones, the cooperation is needed in order for the firm to survive. These are stakeholders such as employees, customers, and suppliers. The second group of stakeholders are not as important for the survival of the company but still have an influential impact on the company’s decisions (Benn et al., 2016, p. 2-3). Value is created by satisfying the requests and cooperation between the both groups (Freeman et al., 2010, p. 24). To best succeed in serving all stakeholders, the first step is to identify all stakeholders (Freeman, 1984, p. 54). This can be done by making a “stakeholder map”, which is a process of objectifying all the groups or individuals affecting or are affected by the organization (Freeman, 1984, p. 25). The use of a stakeholder map can also be useful when envisioning the level influence each shareholder has on the company. Figure 4 is illustrating a stakeholder map with suggestions of common potential stakeholders. In the practice, more categories are often involved and inserted in the stakeholder map (Bourne & Walker, 2005, p. 655).
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Figure 4. Stakeholder Map
Source: Bourne & Walker (2005, p. 655-656), Freeman (1984, p. 25) & Freeman et al., (2010, p. 24)
Critique towards the stakeholder’s theory is that there is a lack definition of stakeholder groups and lack of clear directions of where to focus, which leaves room for managers to operate in their self-interest (Jensen, 2002, p. 238, cited in Mansell, 2013, p. 135). Referring back to Friedman (1962;1970), if corporations are focusing on social responsibility, there is a risk that the free market economy will be threatened. This view is also recognized by Coelho et al. (2003, p. 21) stating that in order for a capitalistic society to run there is a need of capitalists, therefore there should be the first priority for firms over other stakeholders.
2.5.3 The Stakeholder and Shareholder Theories in Relation to AI Implementation
AI has and will continue to have a large impact on societies. It will help to improve many things such as creating value for companies as well as creating opportunities to stakeholders in many forms. There is also a discussion of how far it will affect society and whether it will contribute with positive or negative effects (Floridi et al., p. 590). However, there are downsides and risks of this innovation as have previously been discussed. One can argue that while AI implementation benefits the shareholders of businesses, it might have certain drawbacks to the general stakeholders such as employees. As seen previously, studies have estimated a huge risk of devastating unemployment rates in the future due to AI.
A practical example of having a shareholder approach when implementing AI could be to say that the increased effectiveness of the firm outweighs negative societal impacts such as loss of jobs, since it maximizes shareholder value. This would be somewhat in line with the optimist’s perspective as explained by Frank et al. (2019, p. 6531), since it says that the benefits of AI compensate for the loss of jobs. However, by having an optimist’s perspective, it is also believed that new jobs will come up as a result of AI,
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which benefits stakeholders such as employees and consumers. Having a stakeholder approach to AI would mean to implement or launch a product that improves value for the broadest possible group of stakeholders. Looking at the healthcare industry, it seems that AI implementation benefits both stakeholders and shareholders. We have seen great examples of AI usage that improve human lives, while also possibly maximizing shareholder value and financial performance because of high demand. Furthermore, we have also seen that AI utilizing NPL, such as voice assistants are making life more efficient for both businesses and everyday people, thus serving both the shareholder and stakeholder groups.
In general, it looks like having a stakeholder approach to AI is optimal, since the technology is so useful in all kinds of industries and serves all kinds of people. In most cases, implementing AI technology will increase value for many stakeholders, including shareholders. The most significant drawback is the impact it might have on employees. However, as previously explained by Frank et al (2019, p. 6531), the employee impact depends on which perspective you have. It is too early to know certainly how the workforce will be affected in the coming years by the advancements of AI.
2.6 Disruption Theory
In his book “The Innovator's Dilemma” published in 1997, Clayton Christensen describes the form of competition of disruptive innovation and introduces disruption theory. Disruptive technology is a new way of thinking that can be explained as game-changing innovations that transform industries and take over markets (Christensen, 1997, p. 221- 222). Christensen explains disruption as response to competition. If a competitor updates a strategy, other market players will respond by trying to defend its own strategy and eliminate the threat. The classical pattern of disruptive competition is to satisfy the targeted segments needs in the best way (Christensen, 1997, p. 218).
Market players that are looking to fulfill lower-value searchers will for instance push prices to the lowest, while players targeting higher-value segments add more functions and attributes, as well as better quality. Taking over markets would for instance be a process where the lower-value target would gradually increase value by adding attributes in the early stages of disruption, finally targeting the whole market and winning competition from the incumbent. Eventually origin firms are driven out of the market as the disruption technologies meet the needs of the mainstream market (Christensen, 1997, p. 17-18). One example of this is when Cisco invented the router and competed against Lucent and Nortel and the circuit switching. Lucent and Nortel were focusing on improving the circuit switching and that was what the market segment was asking for at the time, not a router. Lucent and Nortel could either focus on something else than their market segments need. However, when the router was developed it was taking over the whole market. Today, Cisco is meeting new threats from below of other innovative companies in the form of for instance blade servers and soft switching (Denning, p.10, 2016). In the book, Christensen (1997) further explains how market leaders can be outcompeted by missing out on important transformations that are game changing and changes the rules throughout all industries (Christensen, 1997, p. 218). Some other industry examples is when computers shifted from mainframe to PCs, landline changed to mobile phones, filmed photography moved to digital and stock markets went from the floor to online. This was not due to bad management, wrong investment strategies or that they did not satisfy customers’ needs. These companies missed the disruptive innovations
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which created new markets and were able to create lower margins (Murray, 2010, cited in Denning, 2016, p. 10). As mentioned earlier, AI is contributing with new ways of doing things throughout all industries and is changing how things are done. Many processes and implementations of AI are just in the starting phase (Garbuio and Lin, 2019, p. 63). Furthermore, AI is predicted to continue growing to an even larger extent in the next decade (Bini, 2018, p. 2359). The technology can already be seen as disruptive since it has affected many areas. As explained by Purdy and Daugherty (2016, p. 3), AI is making it possible to overcome physical limitations of the traditionally TFP of capital and workforce, by taking form as a capital-labor hybrid. An example of where the use of AI has contributed is in the finance sector as explained previously. AI can be used to provide financial institutions with automatization for insurance advisors. Physical meetings are moving online, and some standardized jobs are being performed by chatbots and robo- advisors.
Behind Christensson´s (1997) disruptive theory an empirical study based on information about disk-drive industries was conducted. The empirical evidence showed that the trajectory of technical innovation exceeded the ability of users, which laid grounds to the disruptive theory (Christensson, p. 38, 2006). In Christenssons research, it was found that market leading companies did best in preserving innovation, but entry level firms beat the incumbent leaders with development of disruptive innovation. This was the correlation found at the time of this research and led to the theory of disruption (Christensson, 2006, p. 40-41)
The theory has been criticized several times. Daneels (2004) provided criticism about the lack of definition of disruptive technologies. Markides (2006) is stating that it is a mistake to apply the theory to all disruptive technologies, since all kinds of technologies have different competitive powers. Therefore, they create different markets, as well as having different challenges for already established firms. Tellis (2006) is also criticizing the methodology of the research and the logic of sampling to test for validity. Lepore (2014) heavily criticized the theory of disruption in an article published in New-York Magazine, with statements about the research methodology behind the theory was lacking. Other problems identified were still the lack of definition of disruptive theory. Since this article was published in the new yorker and not a peer reviewed journal, it was given a lot of attention and criticism was discussed frequently in publications such as The Wall Street Journal, The Financial Times, Business Week, The New York Times, and Salon (Weeks, 2015, p. 417).
The author has responded to the critique, as well as and refined the theory with additions and corrections during the past 20 years. The theory is under an ongoing process of improvement and Christensson (2006) developed a framework of methodology in order to do this. How theory can be tested and improved is by doing new and refined research, from other perspectives of research or other data. When theory cannot explain the outcome, is when there is a possibility of improvement (Christensson, p. 41, 2006).
2.7 Resource-Based Theory
The resource-based theory is used for explaining long-term sustainable financial performance among firms. It explains how superior performance is dependent on company resources, and how different resources have certain attributes which contributes to a superior financial performance (Barney & Clark, 2007, p. 17). The resource-based theory is within the scope of strategic management studies (Barney et al., 2012, p. 110),
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and thus supports strategic management decisions (Barney et al., 2012, p. 112). Barney & Clark. (2007) outlines the theory in different parts in order to present an overview of the theory and its different applications.
First off, in order to understand the theory, it is important to realize how markets sometimes tend to be imperfect, meaning that some of the companies operating in that market are able to gain a competitive advantage over its competitors (Barney & Clark, 2007, p. 31). However, this is not to say that a company that implements a product to create a market imperfection necessarily achieves a competitive advantage, since the costs of launching such a product has to be factored in (Barney & Clark, 2007, p. 32). Furthermore, the companies that utilize such product market strategies have to consider the future value of the products while organizing the necessary resources for the implementation (Barney & Clark, 2007, p. 46). It is advantageous to consider profiting from resources that the companies’ already have under control while implementing a product market strategy, since that increases the likelihood of achieving a competitive advantage (Barney & Clark, 2007, p. 46-47). In the case of AI, following this argument, it is possible that companies in the technology industry are more likely to achieve a competitive advantage. For example, due to controlling valuable resources in terms of capable hardware, as well as research and development. It might also indicate that companies in a sector with a lot of AI potential might face struggles to compete with competitors who have been earlier adaptors, and thus already controls important resources.
Barney & Clark (2007, p. 79) further explains how companies’ internal culture can be contributing to a competitive advantage. A strong organizational culture with well-set values of conducting business has given companies such as Dell and McDonald’s a sustained competitive advantage over its competitors (Barney & Clark, 2007, p. 79). In order to sustain a competitive advantage, it is not sufficient to have a business culture that is only valuable in terms of economic value creation. The culture should also be rare and unique, meaning that few competitors share a similar culture and those who try to imitate it will face a disadvantage, for example in terms of lacking experience (Barney & Clark, 2007, p. 81). Furthermore, one has to remember that there is a distinction between competitive advantage and sustained competitive advantage in the resource-based view. While competitive advantage means that a company is utilizing a strategy of creating value that is not used on the market, a sustained competitive advantage means that such competitive advantage is impossible to copy (Wright et al., 1994, p. 303).
Another important resource of achieving a sustained competitive advantage is the human resource (Barney & Clark, 2007, p. 121). Large corporations frequently express their value of their human resources. For example, IKEA spreads the message that “It’s the people who make the company” (IKEA, n.d). When it comes to human resources (HR) and creating value in a business, it must be addressed how HR can be allocated in order to cut production costs and to increase revenue (Barney & Clark, 2007, p. 122). This can be done by improving the product to be able to charge a premium (Barney & Clark, 2007, p. 122), but also by providing valuable human to human services that improves the customer relationship (Barney & Clark, 2007, p. 123). Furthermore, it has been shown that customers are happier with the services they are provided if the employees are happy with their profession (Barney & Clark, 2007, p. 123). This is logical, since positive and happy employees would most likely have a better attitude with customers. Furthermore, a company could thereby strengthen their human resources by being a satisfactory workplace for its workers. When taking AI into account however, as previously explained
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chatbots and similar services do not create the same satisfaction for customers. On a side note, there is no need to consider a “satisfactory” workplace for a machine, meaning that corners can be cut both in terms of workplace quality and salaries. However, there is a lack of studies regarding if AI can truly replace the value of human workers when it comes to the service industry. Nonetheless, it is safe to say that proper management of human resources is crucial in order to achieve a sustained competitive advantage for firms (Barney & Clark, 2007, p. 141). To strive for a sustained competitive advantage, companies should seek to attain human resources that are rare and hard to copy. This includes both kills, teamwork abilities and workers’ engagement (Barney & Clark, 2007, p. 140-141).
Information technology (IT), is another company resource that has been credited for contributing to increased economic value for firms (Barney & Clark, 2007, p. 143). For example, by using it as an aid to cut costs, as well as making products and services more attractive than those of the competitors (Barney & Clark, 2007, p. 144). Similar to the previously mentioned resources however, the IT implementation must be rather unique and hard to copy in order to achieve a sustained competitive advantage (Barney & Clark, 2007, p. 145). Barney & Clark (2007, p. 145) illustrates an example of gaining a sustained competitive advantage with the help of IT by mentioning General Electric (GE) and their unique way of handling service support via a technique that utilizes call centers. While this is not something unique as of today, it did provide GE economic value, and a sustained competitive advantage at the time. In a similar fashion, one can argue that companies can attain a sustained competitive advantage now by implementing AI technology in a unique way that will be rare and hard to copy for some years ahead.
To conclude the resource-based theory, the main takeaway is that companies should take advantage of the resources and opportunities they have and implement them in a way that is rare and hard to copy by competitors. This will provide economic value and a sustained competitive advantage to the firms’ doing so. Due to the recent AI boom, there are many ways companies can utilize AI technology to differentiate themselves to competitors and thus gaining a valuable advantage.
2.8 Dynamic Capabilities Theory
As previously discussed, the world is rapidly changing in terms of technological advancements. This means that businesses need to constantly adapt to change in order to stay competitive, or even to stay alive in the long run. The theory of dynamic capabilities addresses this and explains how businesses can organize their assets as a way of responding to changes in the market (Kump et al., 2019, p. 2).
Furthermore, the framework of dynamic capabilities consists of three dimensions, or steps that management should undertake in order to stay dynamic (Kump et al., 2019, p. 8). The first dimension is sensing, which means that in order to stay competitive, businesses should be able to sense the environment regarding what opportunities are available or might become available in the near future (Kump et al., 2019, p. 8 ; Chukwuemeka & Onuoha, 2018, p. 11). Relating back to AI, a company might be able to sense a profitable opportunity by implementing AI in some way or form, since it might save them personnel costs, as well as providing innovative products that will have a high demand on the market. In some sectors, it might even be risky not to sense these opportunities, since a lack of implementation might cause a company to lose their competitiveness. This is something Chukwuemeka & Onuoha (2018, p. 11) concluded when analyzing the
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dynamic capabilities framework within fast food restaurants. An example of this could also be seen in the car industry, where a lack of investments into self-driving cars might make a car company become obsolete in a few years. Another example that comes to mind regarding the importance of sensing opportunities is the mobile phone industry, and Nokia’s inability to accurately sense the future of the market (Jia & Yin, 2015, p. 449) and inevitably fall into a decline from being a market leader (Jia & Yin, 2015, p. 446). In general, by sensing the available opportunities, businesses will be able to undertake them and thus staying competitive. This brings us to the second dimension, which is seizing. When several opportunities are identified, the management should organize and spend the resources needed in order to capture the opportunities that are the most fitting in regard to the business (Kump et al., 2019, p. 9). When talking about AI however, it seems that no matter the industry, a company might be able to implement AI in some way, shape or form. Kump et al. (2019, p. 9) mentions that seizing is closely related to a business’s decision making. What comes to mind regarding AI is that a company will probably be able to implement AI to do the seizing itself. As previously mentioned, augmented intelligence and autonomous intelligence can be used to enhance the decision-making process, or even completely make it rely on AI software. This interestingly opens up the possibility of perhaps integrating AI as one of the dimensions of keeping up with the technological advancements. The last dimension, transformation, is when the businesses transform and adapt to change internally. This simply means that companies’ assets are reconfigured continuously in order to put new business models into action and to constantly be ready for new changes in the market (Kump et al., 2019, p. 10). Since AI seems to be the future for most industries, it is important to consider the adaptation of tangible and intangible assets for such a technological change. For example, by preparing employees to work synergistically with artificial intelligence and to prepare to change and adapt hardware for such an implementation
Whilst this framework is very useful in order to understand how to achieve a long-term competitive advantage as a result of technological development, it is useful to consider it in combination to a resource-based perspective (Cavusgil et al., 2007, p. 164). Dynamic capabilities theory does not entirely explain how different capabilities are either built or renewed. In addition, further research of the framework is needed in regard to different settings, such as sectors and geography (Cavusgil et al., 2007, p. 165).
2.9 Summary of the Theoretical Framework and Hypotheses
This theoretical chapter has provided an insight of how AI relates to financial performance through the economic benefits it provides companies with. AI has been shown to increase profitability through the increased efficiency and savings it brings in various sectors, which thus theoretically can be linked to performance measures such as ROA and stock returns. Furthermore, AI is also simultaneously risky due to uncertainties for example regarding its societal effects. This causes an interest of also measuring financial performance in relation to risk as measured by volatility.
The concept of AI usage can also be linked to various theories regarding firms’ performance. For example, the shareholder and stakeholder theories connect AI usage to the social responsibility of firms. From a shareholder’s perspective, AI usage should be in line with firms’ social responsibility, as it brings economic benefits which should contribute to shareholder value maximization. However, there is some controversy regarding the impact AI has on stakeholder value. On one hand, AI benefits consumers
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through its enhancing of life quality as can be seen from self-driving cars in the automotive industry. Furthermore, the usage of AI has shown to benefit people’s health when it is utilized in the healthcare industry. On the other hand, AI might cause negative effects for people who tend to isolate themselves and depend too much on technology. It might also cause governments to spend resources to counteract the new cyber-risks that follow from more advanced AI development. Lastly, AI might have a negative impact on employees and cause high unemployment rates, depending on which perspective one perceives the situation with. It is possible that AI implementation might also create new jobs in a similar matter as the gas stations and diners resulting from the construction of highways. Furthermore, the disruption theory can be linked to a relationship between AI and financial performance. This is with a perspective of seeing AI as a disruptive and new-thinking technology that causes a major disruption in the market. It also has a link with the resource-based theory, since companies who have a wide range of resources and are early adopters of AI in a unique and hard to copy fashion, might gain a sustained competitive advantage. Additionally, the dynamic capabilities framework serves as a useful complement to the resource-based theory, due to it emphasizing long term competitive advantage as a result of technical developments such as AI. Moreover, it accentuates the importance of being prepared for technological change regarding tangible and intangible assets. Interestingly enough, seizing, which is one aspect of the dynamic capabilities framework regarding decision making, might be possible to be performed by AI instead of humans. This is because AI can serve in many ways of enhancing decision making, as well as solely perform the decision making itself through autonomous intelligence.
To fulfill the purpose of this study and explain the relationship between AI usage and financial performance, including both the internal perspective as well as an investor’s perspective with risk in consideration, the following alternative hypotheses will be used:
H1: There is a significant relationship between AI usage and Return on Assets (ROA) for companies on Nasdaq OMX Nordic.
H2: There is a significant relationship between AI usage and Stock Returns for companies on Nasdaq OMX Nordic.
H3: There is a significant relationship between the Standard Deviation of Stock Returns for companies on Nasdaq OMX Nordic.
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3. Scientific Method
This chapter will start out by discussing the choice of the subject, as well as discussing possible preconceptions of the authors. Following that, the different ways of doing research will be examined, including arguments for the chosen methods and way of thinking for this study in particular. The chapter will include the various research philosophies, strategies, and methods which one has to consider when doing a scientific research. Furthermore, the chapter will also have a section regarding the chosen literature. This includes how literature has been found, as well as source criticism. Lastly, the social and ethical considerations in relation to the research process will be discussed.
3.1 Choice of Subject
The authors of this study are Swedish master students in business administration who are specializing in finance. Therefore, it was natural to conduct research relating to financial performance of companies on Nasdaq OMX Nordic. Additionally, due to an interest in technology, as well as the recent boom and implementation of AI among businesses, studying what effects an early adoption of that AI has on companies’ financial performance was seen as interesting. Previously, considerations were made to do research on blockchain technology and its impact on financial performance. However, it was found to be considerably more accessible and accurate data to collect on companies’ use of AI, leading to the final choice of subject.
3.2 Preconceptions
Due to both authors pursuing a master’s degree in finance, there are definitely some preconceptions surrounding the subject. Both authors are interested in the financial outlook of companies and underlying factors that affect financial performance measures such as ROA. Additionally, one of the authors have worked in a bank, resulting in both practical and theoretical competence in finance. Furthermore, both authors have an interest in technology and AI specifically. One of the authors has written an essay about AI in relation to a previous university course. However, the previous knowledge and preconceptions regarding AI is generally limited for both authors and it mostly stems from news articles and other media channels. Furthermore, the research question might be somewhat biased, since the preconceptions are that AI usage should increase financial performance.
3.3 Research Process
It can be of benefit to have a good structure of the process of choosing the most suitable scientific method. The chosen process of this research can be seen in figure 5.
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Figure 5. The Process of this Research
3.3.1 Research Philosophy
The research philosophy influences how the research is conducted, since it is about developing assumptions of one's worldview during the research process (Saunders et al., 2009, p. 108 ; Žukauskas et al., 2018, p. 122). In scientific research there are two main assumptions to consider, namely the ontological and epistemological assumptions (Saunders et al., 2009, p. 109). In terms of how the researcher is viewing the world, one's ontological assumptions represents what one constitutes as the nature of reality (Saunders et al., 2009, p. 597), while epistemological assumptions represent what is constituted as valid knowledge (Collis & Hussey, 2013, p. 47).
Before making assumptions regarding ontology and epistemology however, it is relevant to define the different paradigms, or positions of the research. This is because a research paradigm aids in defining the philosophical assumptions (Žukauskas et al., 2018, p. 123). The two main paradigms concerning epistemology, and that will be considered in this paper are positivism and interpretivism (Collis & Hussey, 2013, p. 43-44 ; Bryman, 2012,
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p. 42). Positivism is defined in various ways by different authors, however Bryman (2012, p. 27) describes positivism as “the epistemological position that advocates working with an observable social reality”. By having a positivistic standpoint, the researcher has an objective view of understanding the world and carries out research that can be scientifically measured and proven (Collis & Hussey, 2013, p. 44). The researcher distances him or herself from what is being analyzed, and thus avoiding involvement of personal biases. (Žukauskas et al., 2018, p. 123). This means that the research approach does not impact the results of the study (Collis & Hussey, 2013, p. 44). On the other hand, interpretivism can be defined as “the epistemological position that advocates the necessity to understand differences between humans in their role as social actors” (Saunders et al., 2009, p. 593). By having an interpretivist standpoint, the researcher has a subjective view of understanding the world (Žukauskas et al., 2018, p. 123), and research is carried out through interaction as a result of an inability to separate what is researched and the researcher’s subjective mind (Collis & Hussey, 2013, p. 45). This standpoint is in direct contrast with positivism, since it is more about exploring and gaining a subjective understanding rather than scientifically proving something. Furthermore, this means that research findings can be perceived differently among researchers (Collis & Hussey, 2013, p. 44-45).
Regarding ontology, the standpoints to take are either objectivism or constructionism (Bryman, 2012, p. 32). These standpoints are directly related to positivism and interpretivism (Žukauskas et al., 2018, p. 128 ; Saunders et al., 2009, p. 111) The view of objectivism is objective, and the social world is external and independent of the researcher (Saunders et al., 2009, p. 596). With this standpoint, it is therefore impossible to influence social phenomena, because it is external to the researcher. Constructionism, also referred to as subjectivism, emphasizes subjective meaning that is transferred to social phenomena (Saunders et al., 2009, p. 111). Similar to interpretivism, constructionism is about making perceptions through a process of interacting with the social phenomena and what is being researched.
3.3.2 Ontological Assumptions
As explained in the previous section, ontology is regarding the nature of reality. With an objective standpoint, the nature of reality is viewed as objective and can be touched and measured. It is thus scientifically feasible to explain and draw conclusions of what constitutes as reality by observing and making independent generalizations (MacIntosh & O’Gorman, 2015, p. 56-57 ; Saunders et al., 2009, p. 129). Additionally, by having this standpoint there is only one reality, which is perceived the same way by anyone that is observing this reality (Collis & Hussey, 2013, p. 47). On the other hand, the subjective standpoint considers reality as a construct of our own perceptions, and it can thus be interpreted and experienced differently (MacIntosh & O’Gorman, 2015, p. 57) In contrast to objectivism, the subjective ontology believes in multiple realities due to the fact that what constitutes as reality is subjectively perceived in various ways by different people (MacIntosh & O’Gorman, 2015, p. 57).
This study has followed an objectivist standpoint to ontology, since it is exploring a statistical relationship between variables. Factual conclusions are therefore drawn on a singular reality that is not perceived differently depending on who is conducting the research. Furthermore, since a subjective standpoint emphasizes meaning instead of facts (MacIntosh & O’Gorman, 2015, p. 60), it would not be feasible for this study.
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3.3.3 Epistemological Assumptions
Regarding what constitutes valid knowledge, the positivist standpoint says that only facts drawn from observing and measuring can be regarded as valid knowledge (Collis & Hussey, 2013, p. 47). Additionally, previous knowledge in terms of theories are analyzed and used for generating hypotheses, which are later statistically tested in the process of generating new knowledge. Following that the statistical conclusions can later be used in order to draw generalized conclusions regarding what constitutes as valid knowledge (Saunders et al., 2009, p. 119). Furthermore, since facts are objective, the researchers’ personal biases from previous knowledge should not affect the conclusions that are drawn. Interpretivists on the other hand believe that valid knowledge stems from subjective meanings and interpretations that are achieved through a close proximity between the researcher and what is being researched. (Saunders et al., 2009, p. 119 ; Collis & Hussey, 2013, p. 47). An example of this could be studies that involve interviewing the research subject (Black, 2006, p. 322-323). Additionally, as a result of knowledge stemming from subjective interpretation, personal biases would affect the conclusions that are drawn.
This study is planning to examine a relationship between variables in terms of solving hypotheses that are grounded in previous studies and theories. The researchers are independent from what is being researched, and facts will objectively be drawn from measuring, testing, and observing from a distance in order to draw generalized conclusions. The positivist standpoint of epistemology is therefore more suited for this research, since the interpretivist standpoint is more about making subjective conclusions based on interpretations.
3.3.4 Research Design and Methodology
Collis & Hussey (2013, p. 4) explains how there are four distinctive research categories to consider when doing research. These categories are exploratory, descriptive, analytical, and predictive. Exploratory research is used when there is very little previous literature to be found regarding a subject (Collis & Hussey (2013, p. 4), or when one wants to study a phenomenon in a new light or point of view (Saunders et al., 2009, p. 139). A simple example could be the interviewing of a particular group of subjects regarding an unexplored subject (Collis & Hussey, 2013, p. 4). Furthermore, Saunders et al. (2009, p. 140) explains the typical exploratory research as someone who is flexible and ready to adapt to new insights or facts that occur. This is arguably because much can be unknown due to the lack of previous studies. Furthermore, exploratory researchers seldom come to a direct conclusion of a phenomena, but rather provides guidance for future studies on the subject (Collis & Hussey, 2013, p. 4). Descriptive research tries to explain already existing phenomena, for example how a sample group is performing their working tasks. (Collis & Hussey, 2013, p. 4). It can simply be explained as a way of accurately portraying either a person, event, or a situation (Saunders et al., 2009, p. 140). Furthermore, the gathering of these characteristics can aid in describing an underlying problem that needs to be studied (Collis & Hussey, 2013, p. 4). Analytical research, also referred to as explanatory research (Saunders et al., 2009, p. 362), is conducted when the researcher wants to study a relationship between variables (Saunders et al., 2009, p. 140). An example of analytical research could be the studying of how a variable such as
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compensation affects the productivity of workers (Collis & Hussey, 2013, p. 4). This type of research is usually done by conducting statistical tests in order to explain a certain causal relationship and how and why something is happening (Saunders et al., 2009, p. 141). It is thus sometimes considered as a continuation of descriptive research, since characteristics are being explained analytically (Collis & Hussey, 2013, p. 5). Lastly, predictive research is the next step of analytical research, which involves analyzing the probability of a relationship or situation to reoccur. It is typically performed by drawing conclusions of one sample’s relationship and later generalizing it on another sample (Collis & Hussey, 2013, p. 5).
Regarding this thesis, analytical research is the most optimal research type. This is considering that this study plans to measure and explain the relationship between the usage of artificial intelligence and the return on assets. It would not be feasible to conduct an exploratory research type, since no new ideas need to be studied. There is already literature in existence regarding both artificial intelligences, as well as financial performance. Furthermore, descriptive research is unnecessary since the characteristics of financial performance, as well as artificial intelligence is already well known. Lastly, a predictive study is not in the scope of this thesis, however, this is something that could be done further on if there is an interest in generalizing the investigated relationship on another stock index for example.
Concerning the research method, there are two approaches that are considered when doing research, namely qualitative or quantitative (Collis & Hussey, 2013, p. 5). Qualitative research methods involve the collection of qualitative data (Collis & Hussey, 2013, p. 6), which is the name for data that is non-numeric. This includes for example interview transcripts and non-numeric answers in questionnaires (Saunders et al., 2009, p. 480). Moreover, due to its non-numeric nature, it is connected to subjective and interpretivist assumptions in regard to ontology and epistemology (Bryman, 2012, p. 399). Quantitative research methods on the other hand are the complete opposite, since they are involving quantitative data collection which is numerical. However, it can also involve qualitative data that can be converted numerically (Collis & Hussey, 2013, p. 5). As a result of its association with numerical data, quantitative research methods are connected to objectivism and positivism in regard to the research philosophies (MacIntosh & O’Gorman, 2015, p. 155).
Due to the numeric nature of this research, as well as the objectivist and positivist standpoints to ontology and epistemology, this research will follow a quantitative method design. The study will involve collecting secondary data of companies’ return on assets, stock return and standard deviation of return, which is in numerical form. Moreover, companies AI usage will be collected from qualitative data and later be quantified and converted to a binomial variable. Later on, relationship testing will occur between financial performance as measured by ROA, and AI usage. This study is not using pure non-numerical data, nor is the objective to make a subjective and contextual analysis of the data. It would therefore be unsuitable to take use of a qualitative method design.
3.3.5 Research Strategy
There are several different research strategies to consider. Which one to choose from depends on the chosen research type, whether it is explorative, descriptive, analytical, or predictive. It also depends on whether the researcher is dealing with a qualitative or
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quantitative method design. Additionally, it is perfectly possible to use a combination of strategies, since they are not mutually exclusive. The research strategies are the following: experiments, surveys, case studies, action research, grounded theory, ethnography, and archival research (Saunders et al., 2009, 141).
Experiments are done when the purpose is to study causal relationships. For example, if one or more independent variables have an effect or induces a change on the dependent variable. Experiments are associated with analytical and exploratory research, and involve an experimental group, as well as a control group that are being tested. The testing works by making some kind of intervention on the experimental group, for example testing a medicine before commercialization, or offering a discount promotion. The control group gets no intervention, making the researcher able to study the difference between the two groups in terms of the dependent variable, in this case perceived medicine effects or purchasing behavior (Saunders et al., 2009, p. 142). The experiment strategy is not feasible for this study, considering that this study does not conduct experiments on different sample groups.
Surveys are popular to use within business research. It is a suitable strategy if the goal is to find out simple descriptive information such as who, what and how something is. Therefore, it is commonly associated with descriptive, as well as exploratory research. By sending out surveys, the researcher collects numerical data that is statistically analyzed in a quantitative manner (Saunders et al., 2009, p. 144). The collection of quantitative data also applies for structured interviews, which is a more personal survey type with social interaction between the researcher and the research subject (Saunders et al., 2009, p. 320). However, this implies that the researcher can be distant, as well as in close contact with the research subject, meaning that this strategy can be considered both for positivists and interpretivists. Although surveys can be useful for collecting quantifiable data, it is not feasible for this study since the plan is to collect a large amount of data about companies from secondary sources.
Case studies are used for studying a phenomenon in its natural context. It is useful when investigating for example why something behaves in a certain way in a specific setting (Collis & Hussey, 2013, p. 68 ; Saunders et al., 2009, p. 145-146). However, case studies can also be used for answering the question as to what and how something is in a certain context. Common data collection methods are for example interviews and observations. It can be performed both in a quantitative manner by collecting quantitative data from questionnaires, as well as qualitative data from interviews that are semi structured. Due to this, case studies are associated with both analytical and exploratory research. Moreover, it is also common to combine quantitative and qualitative data collection methods when doing case studies, which is known as triangulation. (Saunders et al., 2009, p. 146). However, this strategy is not suitable for this study, since contextual behavior is not what this study is intended to explore.
Action research is defined in many ways (Saunders et al., 2009, p. 147), however Collis & Hussey (2013, p. 67) explains it as a strategy used to examine how to effectively deliver an informed change within an environment that is partly controlled. The main idea is thus to make a change in a particular environment and to observe the effects of this change (Collis & Hussey, 2013, p. 67). Moreover, Collis & Hussey (2013, p. 67) mentions that action research is similar to case studies, which is reasonable since it can be conducted in a particular environment or context. Nonetheless, action research is not feasible for this
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study since the plan is not to actively monitor change in a semi-controlled environment.
Grounded theory can be thought of as a theory building process, which is used when trying to explain certain behaviors as a basis of theory building. This is done by making a number of observations which are then used to develop theory (Saunders et al., 2009, p. 149). As previously explained, this study follows a quantitative research design, where the purpose is to test theories by generating hypotheses. This is the opposite of grounded theory, which is used to build theory. Moreover, this study does not try to predict nor explain behavior, which makes this strategy unsuitable.
Ethnography is a research strategy for studying the social world or human patterns through socially acquired knowledge (Collis & Hussey, 2013, p. 65 ; Saunders et al., 2009, p. 149). This strategy involves complete immersion by the researcher in the social world that is being researched (Saunders et al., 2009, p. 149). This strategy would be highly subjective in terms of research philosophy, since the social world can be interpreted in many ways depending on the researcher. Ethnography would not be suited for this research due to the emphasis on social patterns and subjective interpretation.
Archival research is the last strategy on the list. It involves collecting secondary administrative data that has previously been documented by organizations with another purpose than that of the researcher (Saunders et al., 2009, p. 150). This also includes data that is available to be collected from databases (Ventresca & Mohr, 2002, p. 2). This strategy will be used for this research, since data will be collected from the database Eikon, as well as from annual reports and organizational publishing that are available online.
3.3.6 Time Horizon
Time horizons are important to consider, since the researcher must decide whether the research should take time series into account (longitudinal studies), or if the research should only consider observations of variables at a specific point in time (cross sectional studies) (Saunders et al., 2009, p. 155 ; Collis & Hussey, 2013, p. 63).
This study will take a cross-sectional approach to the time horizon, since it is fitting for doing research where different variables are measured at the same point in time (Collis & Hussey, 2013, p. 63. Longitudinal studies are more suited for studying changes over time to see how things develop (Saunders et al., 2009, p. 155). This study is exploring if AI usage has an effect on ROA, stock return and risk, there is of no interest to measure effects over time. Therefore, all that is required is that the variables that are measured against each other are observed in the same point in time. For example, the ROA of company A in 2019 is measured against the AI usage of company A in the same year and that constitutes as one observation.
3.3.7 Research Approach
This chapter has previously touched upon the subject of how theory is used in research. For example, the strategy of grounded theory, as well as the tendency for positivists to regard valid knowledge as something that comes from testing hypotheses generated from theories, which is the opposite of grounded theory. This all relates to whether the
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researcher has a deductive or an inductive research approach, since the chosen approach reflects how theory is used when conducting research (Saunders et al., 2009, p. 124).
The deductive approach is about analyzing existing theory, which hypotheses are generated from and later tested in order to come up with a conclusion that refines the theory (Saunders et al., 2009, p. 124-125 ; Woiceshyn & Daellenbach, 2018, p. 6). It is useful for studying quantitative data and causal relationships between variables. Furthermore, inductive studies usually involve making generalized conclusions of the sample group (Saunders et al., 2009, p. 126), which links it to the objective and positivist assumptions of research philosophies that have been discussed previously. Additionally, one can define deductions as moving from the “general” to the particular”, meaning that hypotheses are formed from what is known and later statistically investigated (Bryman, 2012, p. 24).
The inductive approach on the other hand means that the researcher is building theory as a result of doing the research (Saunders et al., 2009, p. 125). By utilizing research designs that are commonly associated with qualitative studies, such as interviews where the idea is to understand and interpret what is going on, the researcher analyzes the collected data and formulate theory as a result (Saunders et al., 2009, p. 126). The process can be explained as moving from the “particular” to the “general” (Woiceshyn & Daellenbach, 2018, p. 6), and usually involves making observations which general conclusions can be made from (Bryman, 2012, p. 26). Furthermore, inductive approaches are connected to the subjective and interpretivist assumptions of research philosophies as a result of focusing on meaning and interpretation (Saunders et al., 2009, p. 126). A practical example of induction
Inductive studies are important because if only deductive studies are performed, no new theories are generated. This is because deduction only involves refining existing theories and thus removing those who are believed to be false (Woiceshyn & Daellenbach, 2018, p. 6) Furthermore, those preferring the inductive approach usually criticizes the deductive approach for its robust research design which limits the use of additional theories to be used to explain what is going on (Saunders et al., 2009, p. 126). On the other hand, one can argue about the usefulness of theories if they are wrong or outdated. There is definitely a use for deductive studies, particularly within business research where there are already a huge variety of theories in existence. Furthermore, this study will take a deductive approach to theory, since theories and literature regarding AI and financial performance is used in order to form hypotheses that are scientifically tested in order to draw conclusions. An inductive approach would be infeasible due to its qualitative and subjective nature, as well as theories already existing to explain the concepts of financial performance and artificial intelligence. Figure 6 below gives an overall outlook of the deductive process.
Figure 6. The Deductive Process Source: (Bryman, 2012, p. 24)
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3.4 Literature Search Process
The search for appropriate literature started by defining what material was needed in order to formulate a well-structured theoretical chapter and to form hypotheses. Since the plan was to explore Artificial intelligence and its subgroups, as well as financial performance of companies in the Nordic, it was beneficial to come up with certain keywords. These keywords are: Artificial intelligence, AI, machine learning, deep learning, artificial neural networks, financial performance, ROA, stock return and risk. These keywords were put in various reputable literature screening websites, including DiVa, Ebsco, ResearchGate, Google Scholar, SSRN, as well as Umeå University’s library search tool. On these websites, numerous scientific articles and books were found which could be used for basing arguments from, as well as analyzing the existing knowledge around financial performance and artificial intelligence.
3.5 Source Criticism
Information in today’s society is abundant and can be found everywhere. It is therefore important in research to criticize the sources and make sure that they are reliable, in order to make accurate arguments (Hjørland, 2012, p. 1). There are three different kinds of literature sources with varying degrees of reliability, namely primary, secondary, and tertiary literature. Primary literature consists of company reports such as annual reports, emails, conference proceedings and sometimes government publications. This type of literature usually has the highest level of detail and takes the least time to publish. Secondary sources consist mainly of books, journals, and newspapers, which are of less detail but are easier to come across for the researcher. Lastly, tertiary literature have the least amount of detail and are found in indexes, abstracts, dictionaries, and encyclopedias among others (Saunders et al., 2009, p. 69).
The vast majority of sources used in this thesis stems from secondary sources, such as scientific journals, books, and newspaper articles. However, the majority of these are from scientific journals which have gone through a peer review. Peer reviewed articles are a great source of reliable information since experts from the said field evaluate the articles on various points (Hjørland, 2012, p. 2). Much of the information also stems from primary literature such as annual reports that have been published by the company, for example AI usage, as well as financial data from Eikon that is taken from annual reports. Furthermore, in the case of using information from newspaper articles, only well-known newspapers have been used such as Business Insider and Forbes.
3.6 Ethical and Social Considerations
It is important to evaluate the ethical and social effects that the way of conducting research, as well as the results might have. This is especially relevant when talking about AI, as it has been discussed about the various ways it might have an impact on society. For example, it is shown that artificial intelligence effectively increases the return on assets for firms on Nasdaq OMX Nordic, it is likely that managers will be more motivated to implement AI in their business practices. As previously discussed, this can have either a positive or negative impact on the workforce depending on which perspective one has. With a doomsayer's perspective, increased development and implementation of AI would lead to workers being completely replaced, leading to terrifying unemployment rates.
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This could have devastating effects both economically and socially for people losing their jobs, e.g. people losing their self-worth as a result of being replaced as discussed by Turchin and Denkenberger (2018, p. 153). However, those with a more optimistic perspective might believe this to result in positive outcomes, such as new jobs, while boring and non-creative tasks are overtaken by machines. It is also important to remember that thanks to artificial intelligence, the healthcare industry has come up with innovative life changing products which definitely enhances the life quality for some people. If AI is proven to increase financial performance for these companies, they might be more inclined to come up with even more life changing products. On the other hand, if the relationship turns out to be the other way around, those with health care needs might perceive a negative impact of companies reducing the priority of utilizing AI in the healthcare industry. Furthermore, if companies become more motivated to use AI in the energy industry, it will for example aid the developments of smart grids, resulting in more efficient energy usage and less greenhouse gasses emissions. This is also true for the automotive industry, since self-driving cars were shown to be more efficient. Generally, it seems that the results would have a positive environmental impact if it were to be shown that AI has a positive impact on financial performance. However, the social impact is questionable and depends on one’s perspective of AI in the workforce, whether it is optimistic or pessimistic.
Regarding the ethical considerations to be taken in the research process however, not a lot of considerations have to be made for this study since that mostly applies for social research with participant involvement. An example of unethical research would be to study the effects of smoking by encouraging the participants to smoke tobacco, which can be harmful (Bryman, 2012, p. 61). It would also be unethical to conduct research where the research participants are uninformed about what is happening (Bryman, 2012, p. 146)
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4. Research Method
This chapter will examine the chosen sample in terms of countries and industries, as well as presenting the different variables that will be statistically tested in order to measure relationships. Following that, the regression model will be explained as well as relevant theoretical information regarding the model. Lastly, the hypotheses that have been deductively formed from the literature chapter will be presented.
4.1. Census Study
A census study studies is a study including the whole population rather than a sample (Bryman & Bell, 2011, p. 176). The companies on Nasdaq OMX Nordic will be used to represent Nordic companies in general. Nasdaq is the only stock market in the Nordics and since all the listed companies are included in this study, it makes this a census study. The reason for choosing the companies listed on this stock exchange is because it holds a variety of large companies from different industries. Furthermore, as previously mentioned the companies in Nordic countries are particularly open to technology, which makes them an interesting population to conduct research on. Due to a few of the companies on the stock exchange not having available data in Eikon, 11 companies were excluded.
4.2 Data Collection
The data collection process started by screening all the listed companies on Nasdaq OMX Nordic in order to find large public companies on the Nordic markets. Collection of information regarding which industry and country of registration the company operates under was available on the Nasdaq webpage. Information of the companies on Nasdaq OMX Nordic and their AI usage was collected from company reports, such as annual reports, as well as from newspaper articles and the companies’ websites. Thereafter, financial data was collected from Thomson Reuters Eikon which is available through the Umeå University library. The data in Eikon stems from annual reports, resulting in a sample consisting of both primary and secondary data. The 400 largest Nordic public companies were displayed after using the screener tool in Eikon. Then, data for each company was manually picked out in order to measure financial performance, risk and size and thereafter downloaded to Microsoft Excel.
The time period stretches from the selected period of 2015-2019 and was chosen due to the nature of the study. When researching companies and their implementation of AI, it was found that most companies had implemented it in recent years. Usage of AI is a rather new phenomena and in the years before 2015, it was not used by most companies in the population. However, the years 2018 and 2019 were when a large number of companies had implemented AI. Therefore, these years are included in the study, as well as 2015, 2016 and 2017 as a well weighed reference in order to get a broader perspective. Data was collected on an annual and weekly basis, but in the end, all finished data where transformed to annually before importing it to Stata.
Due to a few companies not having available data on Eikon when having the screener set on the Nordic countries, they had to be excluded. The research and development variable
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was planned to be included in this study, but due to missing observations this variable was excluded. Finally, it resulted in a sample consisting of 152 companies and the total number of observations amounted to 721 in the ROA model 720 in the Stock Return model and 714 in the Risk model.
4.3. Variables
4.3.1 The Dependent Variables
Since this study uses an analytical research strategy, one dependent variable and at least one independent variable is used in order to measure a possible relationship where the dependent variable is affected by a change in the independent variables (Saunders et al., 2009, p. 142 ; Collis & Hussey, 2013, p. 204). This study is examining a relationship between Financial Performance and AI usage in the Nordics. Financial performance will be measured in the three dimensions of; ROA, Return and Risk (standard deviation). Therefore, three regression models will be conducted.
ROA is a measurement of a company’s net profit, divided by its total assets (Leach & Melicher, 2018. p. 64) and is thus a financial ratio that indicates how a firm is performing financially (Leach & Melicher, 2018, p. 164). ROA is widely used and is available in companies’ balance sheets from the financial statements (Pattiasina et al., 2018, p. 1-2). It was thus found to be a suitable measurement of financial performance in a regression analysis where multiple companies are analyzed. ROA was collected in Eikon and downloaded to excel as a yearly percentage growth and did not have to be modified.
To investigate AI usage from an investor perspective, stock return was included in this study. In order to get a fair overview of returns over the year and exclude bias due to collect data from only one snapshot of time, an average of the returns were created. Weekly closing prices between the years of 2015 and 2019 plus one week from 2014 was collected for the calculations, the last week of 2014 was needed to calculate the return of the first week in 2015 which was in total 260 weeks.
In order to calculate weekly returns, weekly closing prices (Pt ) for all stocks was divided by the previous week(Pt-1 ). Thereafter, it was subtracted by one in order to get the value in weekly percentage growth [(Pt/Pt-1) – 1] = Rt. Thereafter, an average of all the 52 weekly observations were created into a yearly average, by using the AVERAGE command in excel.
Investing in new technologies can be seen as a large risk, since there are uncertainties of the future outcomes. However, a risky investment might also turn out to be highly profitable. The potential outcomes of using AI are risky. As explained in studies such as Floridi et al, 2018 ; King et al. 2018 ; Matt et al. 2015; Frank et al., 2019 ; Tekic & Koroteev, 2019, investing in new technologies may result in new challenges and uncertainties, as finding the right implementation strategy in order to succeed. Investing in AI might create fluctuations in the stock market. Furthermore, risk and return is expected to have a positive relationship as investors are demanding compensation for taking on riskier investments (Chiang & Doong, 2001, p. 307).
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A common variable of measuring risk is standard deviation (SD) (Daly, 2008, p. 2379). It was found that no variable for SD was available in Eikon. Therefore, the weekly closing prices of stock return (the same ones collected for the stock return) were used for calculating SD. By using the STDEV command in excel when including data of return for 52 weeks for each of the years, SD for all years was computed and the variable return was constructed.
4.3.2 Independent Variable
The main independent variable in this study is AI usage. A statistically categorical outcome had to be found due to AI usage in companies being a yes or no question, and an applicable measurement is “companies using AI and companies not using AI”. Therefore, a binary independent (zero-one) variable if a company is using AI or not will be used.
When the list of companies on Nasdaq OMX were collected, information about whether these companies had implemented AI and in which year was collected. This was done by researching online and visiting the companies web pages and annual reports. Thereafter, judgements of the years the companies had implemented AI were made. Since the sample included companies from Sweden, Denmark, Finland and Iceland, search words on the respective languages were used in order to get a better grasp of sources and to better determine the year of AI implementation. Synonyms, related words to AI and abbreviations, as machine learning, automation, AI, and ML were used to improve the research. If AI was implemented in year X, it was assumed that the companies used AI all subsequent years also. This is because most companies have implemented AI in most recent years (2017-2019). For some early adopters, as for instance 2010, the current usage of AI was researched and double checked. Further details about the process and sources behind the underlying judgement of what year companies implemented AI can be found in appendix 1.
4.3.3 Extraneous Variables
Extraneous variables are variables that act as independent variables, impacting the dependent variable (Collis & Hussey, 2013, p. 204). These variables are important to include since it helps to explain the influence the independent variable has on the dependent variable. This is done by eliminating noise and contributing to a more specified correlation between the main independent and the dependent variables (Seelig et al., 2008, p. 408). The extraneous variables that will be included in this research are Size, Country and Industry.
Market capitalization is a measurement of the value of the stock times the total amount of a company's outstanding share (Leach & Melicher, 2018, p. 248). Market capitalization is therefore seen as a suitable variable measuring firm size. By including this as an extraneous variable, it will contribute to eliminate the bias related to size. For instance, there is a chance that size impacts financial performance in firms. The resource-based theory explains how superior performance and competitive advantage is dependent on company resources (Barney & Clark, 2007, p. 17). This argument could be interpreted as large companies are more likely having a positive relationship with financial performance. In terms of risk, the arguments of the resource-based theory could be
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interpreted as if size leads to a more secure financial performance, risk might be lower in large companies. Market capitalization was collected in SEK from Eikon since most of the firms in the population are Swedish. No further modifications had to be done with the variable.
Industry was included as an extraneous dummy variable in this study. It is interesting to see whether industry differences have any explanatory contributions to the relationships. The variable was collected manually from Nasdaq´s webpage for each of the companies. The industries of the companies in the data set were categorized as; Industrials, Consumer goods, Basic materials, Healthcare, Financials, Consumer services, Oil and gas, Telecommunications, Technology and Utilities.
Country is the last extraneous dummy variable included in this study. Similarly to industry, it was deemed interesting to explore whether there are differences in the relationship between AI usage and financial performance in the different Nordic countries. In this study, it is assumed that Nordic countries are similar and therefore analyzed as one population. However, in case there are differences among the countries, including this extraneous dummy variable will contribute to exclude noise of the model. The variable was also collected manually from Nasdaq´s webpage and includes Sweden, Denmark, Finland and Iceland.
4.4 Regression Analysis
A regression analysis is a model examining relationships among variables. The aim is to explain movements of the dependent variable by the quantification of the independent variable (Studenmund, 2014, p. 5). When there are more than one independent variable the regression is referred to as multiple regression analysis (Studenmund, 2014, p. 13). Studenmund (2014, p. 13) denote the multiple regression analysis as follows;
Equation 1
Where the Y is the dependent variable, 훽0 is the constant or the intercept, which indicates the value of Y when X equals zero (Studenmund, 2014, p. 7). The other 훽´s are the estimated regression coefficients, and Y the independent variables. There are always factors affecting Y, that cannot be explained as to which and how many of the X variables (independent variables) that are included. There will always be unknown and unpredicted influences in the form or errors or any other impact, which is represented by 휖. The term is called the stochastic error term and is representing the factors of the Y variable that cannot be explained by the included X variables (Studenmund, 2014, p. 9). The aim of this research is to investigate the relationship between the dependent variables as measured by; ROA, Stock Returns and the returns in relation to risk. As well as the AI usage of companies, which is the independent variable. The extraneous variables are; size, industry and country. The multiple regression model will test the significance of the relationship between the dependent variable and the independent and extraneous variables (Studenmund, 2014, p. 13). A regression analysis cannot confirm causality, but can identify the direction and strength of the relationship (Studenmund, 2014, p. 6). Therefore, the regression analysis will be analyzed together with previous studies in order to strengthen the findings.
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4.4.1 Coefficients
The regression coefficients are represented by the 훽´s in equation 2. The coefficients for each independent variable are estimated in a regression model, which is the direction and strength the X variable has on the Y variable. The particular regression coefficients are affected by unit-increases of X1i , on the dependent variable Y, when the other X2i X3i are kept constant. In Equation 2, the “beta-hats” are representing the empirical best possible estimations found from the regression model. This is a sample estimation of the real- world regression coefficients of the whole observed population of X’s and Y’s (Studenmund, 2014, p. 15).
Equation 2
4.4.2 Dummy Variables
The variables AI, Country and Industry are categorical and will be set as dummy variables in the regression analysis. This is useful when a concept that is qualitative has to be quantified (Studenmund, 2014, p. 12). Companies using AI will be set to 1 and companies not using AI set to 0 (Studenmund, 2014, p. 238-239). In the variables Industry and country, companies will be categorized from 1-10 and 1-4 respectively. To clarify the interpretation of the analysis of the dummy variables, companies for instance not using AI are set as a 0 and are a reference variable. For country and industry, one category will be set as a reference variable, and this will as well be the reference variable which the other variables are compared to. When analyzing dummy variables, the real probability can never be observed since it reflects a situation before a concrete choice has been made. After the decision is made, the dependent variable can in this analysis only take values between 0 and 1, 1-4 and or 1-10, even though the expected values of probability can take numbers anywhere in between (Studenmund, 2014, p. 418-419).
4.4.3 Ordinary Least Squares (OLS)
Ordinary Least Squares (OLS) is a widely used technique measuring regression coefficients. To reach as close-to-reality results as possible and increase the quality of the regression analysis, the squared residuals are supposed to be minimized (Studenmund, 2014, p. 36-37). OLS has seven requirements that have to be fulfilled in order to be a good estimator. These assumptions are listed below.
1. There is linearity in the regression model and exists an error term. 2. The population mean of the error is zero. 3. The explanatory variables used in the regression model does not correlate with the error term. 4. No serial correlation - The error terms are uncorrelated (residuals are uncorrelated). 5. No heteroskedasticity - The variance of the error term is constant. 6. No perfect multicollinearity exists - none of the explanatory variable(s) are perfectly correlated. 7. The error term is normally distributed.
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4.5 Hypotheses
Before performing a statistical test, a hypothesis has to be clarified. This is important in order to prevent potential biases and changes due to findings along the way. The null hypothesis “H01” represented the results not expected by the researcher while the alternative hypothesis “HA” represented the expected results (Studenmund, 2014, p. 128- 129). The hypothesis that will be tested in this research is listed below.
H01A: ROA of Nordic companies has no significant relationship with the implementation of AI
HA1A: ROA of Nordic companies has a significant relationship with the implementation of AI
H02: Stock Return in Nordic companies has no significant relationship with the implementation of AI
HA2: Stock Return in Nordic companies has a significant relationship with the implementation of AI
H03: Risk of Nordic companies has no significant relationship with the implementation of AI
HA3: Risk of Nordic companies has a significant relationship with the implementation of AI
The statistical formulation of the hypotheses is presented below.
H01: 훽ROA = 0
HA1: 훽ROA ≠ 0
H01: 훽Return = 0
HA1: 훽Return ≠ 0
H01: 훽Risk = 0
HA1: 훽Risk ≠ 0
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4.6 Regression Models
Below, drafts of the regression models will be presented.
Equation 3
Where,
ROA: Dependent variable, yearly financial performance measured by ROA of observed company.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
Equation 4
Where,
Return: Dependent variable, yearly financial performance of observed company measured by stock return. Yearly average of weekly stock returns.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
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Equation 5
Where,
Risk: Dependent variable, yearly financial performance measured by risk of observed company. Yearly risk of weekly standard deviations.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
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5. Data
This chapter will contain information regarding the data, such as descriptive statistics and the variables market distribution. Subsequently, the OLS assumptions will be presented and analyzed whether they are fulfilled or not. After that, the chapter will end up with the finalized regression models.
5.1. Descriptive Statistics
Descriptive statistics for the variables included in the study is presented in table 1. From the displayed data, no extreme values or outliers could be observed and therefore no changes had to be undertaken. As can be seen, there were some missing observations in the data for the variables ROA which had 750 observations, market capitalization which had 721, Return which had 723 and SD with 718. For the variables manually collected; AI usage, country and industry of operation, all observations were available which were 760 in total. However, since each observation requires all variables, the regression model will automatically count for the lowest number of 718 observations.
As can be seen from table 1, the mean of AI was 0.197, meaning that the predominant number of companies and their observed years did not use AI. The mean of ROA on the observed companies from Nasdaq OMX Nordic was 7.8%. The largest ROA was 45.9% and the lowest was -24.2%. Return has a mean of 0.2%, a minimum value of -3.9% and a maximum of 2.6%. Risk had a mean of 3.4%, minimum value of 1.6% and maximum of 12.%. Market capitalization had a mean slightly above 72 billion SEK, a minimum value of 152 million SEK and a maximum value of 1020 billion SEK. Since the country and industry code are categorical values set to multiple numbers, the descriptive statistics of mean and SD cannot be expressed.
Table 1. Descriptive Statistics
5.1.1 Two-Sided T-test
Since the independent categorical variable AI is divided into two subgroups, a two-sided t-test on a 5% significance level was conducted in order to observe the distributions of the values of the dependent variables. In figure 7, 8 and 9, the distributions of companies using AI and not using AI is displayed for each of the regression models. As can be observed, the distributions in the two subgroups are normally distributed and are looking similar in the both categories. This which indicates that there are not any large and disturbing differences in the data.
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Figure 7. Histogram of ROA and AI
Figure 8. Histogram of Return and AI
Figure 9. Histogram of Risk and AI
The two-sided t-test for each of the dependent variables and the two groups of AI usage is displayed in table 2, 3 and 4. It can be observed that the number of observations for the respective variables do differ. This is because there were different missing observations of the variables in the dataset. The same pattern can be observed in the tables. Mean,
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standard error and standard deviation of the values on the 95% confidence interval are in general very similar for the subgroups for all the dependent variables. However, small differences can still be observed. For ROA, the mean for companies not using AI is higher than for companies using AI with a value of 8% and 7% respectively. This means that the ROA is generally higher for companies not using AI. In the two groups of return, the mean for companies not using AI is also higher with a value of 0.2% compared to companies who are using AI that have a value of 0.1%. For the two groups of risk, the mean for companies who have not implemented AI is lower with a value of 3.9% compared to companies who have implemented AI which have a mean of 4.4%.
Table 2. T-test ROA
Table 3. T-test Return
Table 4. T-test Risk
5.1.2 Correlation Matrix
The correlation matrix is computed in order to see if there is any potential correlation between the variables. This is important to investigate since if there seems to be a high correlation between variables, it can be difficult to determine the individual effect on the dependent variable. This would intend that the effect of the coefficients in the regression analysis would be less reliable (Newbold, 2013, p. 461). The correlation values can vary between 1 and -1, where 1 is a perfectly positive dependency and -1 is a perfectly negative dependency (Newbold, 2013, p. 162). If the value from the correlation matrix is below | 0.1 | no correlation can be implied, if the values are between | 0.1 | and | 0.3 | the correlation is small, values between | 0.3 | and | 0.5 | imply moderate correlation and if the value is above | 0.5 | it implies a significant correlation (Cohen, 1988, p. 77-81). All variables
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were set in one correlation table, however, the correlations between variables not included in the same regression models can be ignored. As can be seen from table 5, ROA has a negative correlation with AI of -0.072, meaning that financial performance in terms of this measurement is decreasing as implementation of AI increases. ROA has a small, positive correlation with market capitalization, meaning a small increase with size. A weak correlation can be observed between return and AI, showing a number of -0.101. Market capitalization shows no correlation with return, with a small number or -0.044. Risk has a weak correlation with AI of 0.142, meaning that risk increases softly with the use of AI. Risk and market capitalization has a weak negative correlation of -0.101, showing that there is a relationship of risk which is decreasing with size. Market capitalization has a small correlation 0f 0.203 with AI, meaning that AI has a weak increase with size of company. According to the correlation matrix, no strong correlations can be displayed and therefore, no problems of correlation can be identified.
Table 5. Correlation Matrix
5.2 Market Distributions
Figure 10. Industry Distribution
In figure 10, the distribution of industries among the companies can be seen. The total number of companies included in the study were 152. Among them, within the industrial sector amounted to 40 companies, consumer goods included 14 companies, basic materials 12, health care 20, financials 42, consumer services 10, oil and gas 5, telecommunications 5, technology 5 and utilities 1.
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Figure 11. Country Distribution
In figure 11, the spread of countries of registration can be displayed. Among the 152 companies, 88 were registered in Sweden, 37 in Denmark, 27 in Finland and 2 in Iceland.
Figure 12. Percentage of Companies Using AI Between 2015-2019
In Figure 12, the percentage of companies that have implemented AI and have not implemented AI for respective years can be seen. It has increased from close to no companies using AI in 2015 to almost half of them using AI in 2019.
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Figure 13. Growth of Number of Companies Using AI Between 2015-2019
In figure 13, it is possible to see the growth of the amount of companies that had implemented AI between the years 2015-2019. Between 2015 and 2016, 6 companies implemented AI. Between 2016 and 2017 this number had increased by 11. Between 2017 and 2018, the increase amounted to 30. Between 2018 and 2019 the increase was a bit lower with 23 companies implementing AI.
Figure 14. AI Implementation for All the Observed Years
The percentage of the observed years which companies had implemented AI can be seen in figure 14. The total number of observations of years was 760 (152 companies and 5 years of observations), where 150 of the observed years had implemented AI and in 610 of the observed years the companies had not implemented AI.
5.3. Ordinary Least Square Assumptions
As mentioned in the previous chapter, in order to use OLS as a reliable statistical tool, the seven assumptions have to be fulfilled. These assumptions will be tested and tested below.
5.3.1 Linearity (Assumption 1)
The first assumption in OLS is that there is a linear relationship among the dependent variables to each of the independent variables. The residuals represents the difference
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between the observed values and the fitted values estimated in the regression (Studenmund, 2014, p. 16). To check if there are any complicated patterns or relationship between the dependent and the independent variable, and confirm linearity, a scatterplot of the residuals and the fitted values can be displayed (Newbold et al., 2013, p. 515-516).
Figure 15. Two-Way Scatterplot of ROA as Fitted Values Plotted Against Residuals
In figure 15, the residuals are plotted against the fitted values of the ROA model. The observations are randomly distributed around 0, no unusual patterns can be observed and the assumptions of linearity can thus be confirmed. However, it is possible to see that the observations take some clustered values, which is most probably an outcome from the categorical values included in the model. The assumption of random distribution, mean of zero and linearity does still hold.
Figure 16. Two-Way Scatterplot of Return as Fitted Values Plotted Against Residuals
Observing figure 16, it is possible to see that the residuals are randomly distributed around 0 which indicates linearity. The weak pattern of clustered observations similar to figure 16 can be observed in this plot, which is as well explained by the dummy variables, taking similar values.
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Figure 17. Two-Way Scatterplot of Risk as Fitted Values Plotted Against Residuals
A similar pattern can be observed in figure 17, no unusual patterns can be found and linearity can be confirmed. Similarly to the previous plots, a weak pattern can be observed in the figure due to the dummy variables.
From observing all the above scatter plots, it can be concluded that all the dependent variables used in the regression models fulfills the first assumption of OLS about linearity. None of the models had to be modified to achieve linearity.
5.3.2 Error Term (Assumptions 1, 2, 3 & 7)
In the first assumption of OLS, it is stated that an error term exists, which most regression models automatically do. As previously mentioned, the stochastic error term represents the unknown and unpredicted influences, which are all the factors of Y that cannot be explained by the X´s included in the model (Studenmund, 2014, p. 9). The second assumption is that the residuals have a population mean of zero. According to Wooldridge (2016, p. 32-33), in OLS, residuals do always have a mean of 0. By taking the mean of the residuals in STATA, the results showed a small value of 2.32e-12 for the ROA regression, -1.15e-11 for the Stock Return regression and -1.71e-12 for the risk regression. The values are close to zero and therefore it can be concluded that the second assumption is fulfilled. The third assumption states that the correlation between the error term and independent variables has to be zero. According to Wooldridge (2016, p. 32- 33), residuals have a mean of zero and the correlation between the residuals and the independent variables are always zero in the construction of OLS.
The seventh assumption suggests normal distribution of the error term, this was checked by doing histograms for each of the regression models.
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Figure 18. Histogram ROA
Figure 19. Histogram Return
Figure 20. Histogram Risk
In figures 18, 19 and 20 the distribution of the error terms of each respective model is presented. The estimated lines are very close to the lines of normal distribution of a 95% confidence level and therefore normal distribution of the error terms can be confirmed.
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5.3.3 Autocorrelation (Assumption 4)
The fourth OLS assumption states that the observations of the error terms have to be uncorrelated with each other. In case the error terms are correlated, autocorrelation exists. This violation is most common in time series data and in panel data, since it involves data over time (Studenmund, 2014, p. 322). However, Kim. (2019, p. 4-5) is stating that when conducting cross-sectional studies, it can be assumed that the error terms are independent from each other.
Since the data in this study is cross sectional, it can be assumed that there is no autocorrelation in the dataset. The dataset can be assumed to not suffer from autocorrelation since the time series for observations are independent from each other and that the fourth assumption is fulfilled (Studenmund, 2014, p. 322).
5.3.4 Heteroscedasticity (Assumption 5)
Assumption five states that there should be no hetroscadacity in the model, meaning that the variance of the error term should be constant. This implicates that no observed values should be correlated with each other or any other variables in the regression (Studenmund, 2014, p 102-103). One way of controlling this is to perform a Breusch-Pagan test in Stata. The test is analyzing if the independent variables have independence in the relation of the error terms (Klein et al., 2016, p. 570). If dependency exists, there is a problem of heteroscedasticity, violating the regression model (Klein et al., 2016, p. 568). A Breusch- Pagan test was done for each of the regression models in order to analyze if they suffered from heteroscedasticity.
Table 6. Heteroscedastic Test ROA
Table 7. Heteroscedastic Test Return
Table 8. Heteroscedastic Test Risk
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At a 95% confidence level, the tests are providing a probability where values of chi2 lower than 0.05 were assumed to suffer from heteroscedasticity. The observed chi2 values in table 6, 7 and 8 are all higher than 0.05, thus the null hypothesis of constant variance cannot be rejected, and all models suffer from heteroscedasticity. One alternative to resolve these problems is done by including the command vce (robust) to the regression model in Stata (Kalita, 2013, p. 14).
5.3.5 Multicollinearity (Assumption 6)
Collinearity between the independent variables would indicate that those variables are measuring the same thing. This would complicate the OLS model since variables would be indistinguishable. Multicollinearity can be solved by for instance dropping variables from the regression formula (Studenmund, 2014, p. 103). Whether a model suffers from multicollinearity can be investigated by testing the variance inflation factor (VIF). Variables taking a value over 10 have to be further investigated. As can be seen in table 9, 10 and 11, the values were ranging between 1.03 and 2.43 which means that those are close to having no linear relationship. Therefore, it could be concluded that there is no multicollinearity in any of the models.
Table 9. VIF Test ROA
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Table 10. VIF Test Return
Table 11. VIF Test Risk
5.4 Final Regression Models
In this chapter, several statistical tests have been performed and it has been concluded that the assumptions of OLS is accepted besides assumption 5 of heteroscedasticity. Therefore, robust standard error terms were added in all of the three models. The data did not need any further modification. Therefore, the draft of the regression model can be used as the final model. The final regression model is therefore constructed as follows;
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Equation 6
Where,
ROA: Dependent variable, yearly financial performance measured by ROA of observed company.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
휺r: Robust standard error term.
Equation 7
Where,
Return: Dependent variable, yearly financial performance of observed company measured by stock return. Yearly average of weekly stock returns.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
휺r: Robust standard error term.휖휀
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Equation 8
Where,
Risk: Dependent variable, yearly financial performance measured by risk of observed company. Yearly risk of weekly standard deviations.
Intercept: Intercept.
B 1-5: Coefficients.
AI: 1 if the observed company has implemented AI, 0 if the company has not.
Risk: Yearly risk of weekly standard deviations.
Size: Yearly observations of market capitalization in the last date of the year.
Industry: Dummy variable of industry observed company operates in.
Country: Dummy variable of the country the company operates in.
휺r: Robust standard error term.
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6. Results
This chapter will present the results of the regression models. After that, a summary of the hypotheses will be included, as well as a discussion regarding the truth criteria in relation to quantitative studies.
6.1 Multiple Regression Model of ROA (OLS Robust)
In table 12, the results from the multiple regression model where ROA where the dependent variable are presented. Each variable will be presented and discussed in separate sections below. How much power the independent variables have on explaining the dependent variables is represented by the R-Square. The value is measured between 0 and 1, where a higher value indicates a higher explanation power of the independent variables (Hamilton et al., 2015, p. 152). The R-square has a value of 18.24% which indicates that there are many other external factors explaining the ROA in Nordic companies. One explanation behind a low R-square can be that too few explanatory variables has been included. A potentially higher R-squared could have been reached with more variables included (Studenmund, 2014, p. 54).
Table 12. Multiple Regression Model ROA
6.1.1 ROA and AI
On a 95% confidence level, p-values have to be lower than 0.05 in order to be significant. As can be observed from table 12, the p-value for the AI coefficient is 0.001 which
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indicates that the coefficient is significant. This can also be seen looking at the Z-values, which is testing the alternative hypothesis that coefficients have different values than zero. To accept this alternative hypothesis and reject the null hypothesis level on a confidence level 95%, the z-value has to take a value higher or lower than | +/- 1.96 |. The Z-value for the AI coefficient is -3.37, which means that null hypothesis can be rejected in favor of the alternative. The estimated coefficient has a value of -0.023%, indicating that a company that has implemented AI has a ROA of -2.32% lower compared to companies who have not implemented AI. On a 95% confidence interval, the model tells us that the coefficient lies between the values -3.57% and -1%. This means that even though the coefficient has a soft negative value, it is on a 95% confidence interval a negative value.
6.1.2 ROA and Market Capitalization
In table 12, it can be confirmed that the coefficient of market capitalization is significant on a 95% confidence interval since the p-value is 0. The market capitalization has a coefficient of 1.67e-13 SEK which can be interpreted as when market capitalization increases with one unit, which is 1 SEK, the ROA increases with a small decimal number of 1.67e-13. This is a positive correlation, although it is very small. Since the observed companies are large in size and are having market capitalizations of billions, this can be more easily interpreted by multiplying the numbers. If market capitalization would increase by 10bn SEK, ROA would increase with 0.00157%. On a 95% confidence interval, the coefficient lies between the values of 9.26e-14 SEK and 2.40e-13 SEK.
6.1.3 ROA and Country
The countries are numbered as 1. Sweden, 2. Denmark, 3. Finland 4. Iceland. Since this is a dummy variable, one of the categories had to be set as a reference variable and Finland was chosen in this case. This choice was made since it provided most significant p-values thus the simplest interpretation. The p-values of 0.002, 0.015 and 0.02 are all below 0.05 which means that the coefficient measured by the regression analysis is significant. Finland has a mean ROA of 5.9% and the coefficients are measured in relation to that number. This means that Sweden, Denmark, and Iceland have a larger ROA than Finland by respectively 2.27%, 2.26% and 2.23%. Finnish companies have a lower ROA in comparison to the other countries in the population. However, the difference in ROA between the countries is very low.
6.1.4 ROA and Industry
The industries are numbered as 1. Industrials. 2. Consumer goods. 3. Basic materials. 4. Health care. 5. Financials. 6. Consumer services. 7. Oil and gas. 8. Telecommunications. 9. Technology. 10. Utilities. Similar to the country variable, industry is a dummy variable and therefore a reference variable has to be set. The first category; Industrials, was chosen as a reference category since it provided the model with the most p-values and was thus easiest to interpret. As can be seen from table 12, category 2; consumer goods have a p- value of 0.00, 4; health care has a p-value of 0.00, 5; financials have 0.00, 6; consumer services 0.005, 8; telecommunications 0.004 and 9; technology 0.036. All of these coefficients are significant on a 95% confidence level and can therefore be explained by the regression model. Category 3; basic materials had a p-value of 0.81, 7; oil and gas
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had a p-value of 0.443 and utilities of 0,381. These P-values are taking higher values than 0,05 and can therefore not be explained from this regression model on a 95% confidence level. Industrial has a mean ROA at 7.2% which means that the other coefficients are compared to this value since it is the reference category. The significant coefficient for consumer goods is 4.18% which means that companies within the consumer goods industry have a higher ROA than industrial companies. Health care companies are having a coefficient of 4.02% which also show a positive relationship with ROA in comparison to the reference category. Financials have a coefficient of -2.13%, meaning that there is a negative relationship and the ROA is lower in comparison to the reference category. Consumer services companies have a coefficient of 5.26%, telecommunications -4.23%, and technology 5.99%. The categories that cannot certainly be explained from the model are; basic materials, oil and gas and utilities have coefficients of respectively -0.18%, 1.42% and 1.78%.
6.2 Multiple Regression Model of Stock Return (OLS Robust)
In table 13, an R-square of 4.98% can be seen, which is much lower than in the previous presented regression model of ROA. This indicates that this regression model with stock return as a dependent variable can be explained by these independent variables to a smaller extent than what ROA can.
Table 13. Multiple Regression Model Stock Return
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6.2.1 Stock Return and AI
The p-value for the AI coefficient is 0.004, which is below 0.05. Therefore, the null hypothesis can be rejected, meaning there is a significant relationship between AI usage and Stock Return on a 95% confidence level. The coefficient has a value of -0.14, which indicates a minor negative relationship between companies using AI and stock return, in comparison to companies not using AI. The value of the coefficient lies between -2.4% and -0.5% on a 95% confidence interval.
6.2.2 Stock Return and Market Capitalization
As can be seen in table 13, Market capitalization has a p-value of 0.817. The null hypothesis is therefore accepted, since the coefficient is not significant on a 95% confidence level. The coefficient has a value of -2.32e-15 and the value lies between - 5.78e-15 and 1,12e-15 on a 95% confidence interval. This indicates that the number is most likely very small, and that the correlation is weak. However, it is unclear whether it is positive or negative and cannot certainly be explained by this regression model.
6.2.3 Stock Return and Country
Since country is a dummy variable, a reference category had to be used in this case. However, none of the country variables that was set as a reference category provided a model with significant results. Therefore, Sweden was randomly chosen. As can be seen in table 13, Denmark, Finland and Iceland have p-values of respectively 0.602, 0.281 and 0.410 and all the null hypotheses are therefore accepted. This means that there is no significant relationship between Stock Return and country. The mean stock return for Swedish companies is 0.0018% and the coefficients for Denmark has a value of -0.03%, Finland has -0.06% and Iceland 0.15%. The 95% confidence interval ranges between - 0.2% and 0.49%, which also shows that it is unclear whether the coefficients have negative or positive relationships with Stock Return.
6.2.4 Stock Return and Industry
The best suited reference category for this model was found to be category 4; health care companies. Category 1; Industrials have a p-value of 0.007, 2; consumer goods have a p- value of 0.0, 3; basic materials’ p-value is 0.015, 5; financials have a p-value of 0.0, 8; telecommunications has a p-value of 0,01 and 10; Utilities has a p-value of 0.035. All of these p-values are above 0.05, the null hypothesis can be rejected and significant relationships with return can be confirmed on a 95% confidence level. The p-values for category 6; consumer services is 0.215, 7; oil and gas is 0.684 and 9; technology is 0.508. These are all above the significant level 0,05 and the model can therefore not confirm a significant relationship with return. The mean return for health care which is the reference category is 0.354%, and is the reference value for the estimated coefficients. category 1; industrials have a coefficient of 0.18%, and therefore a lower return than health care companies. 2. Consumer goods has a coefficient of -0.28, 3. basic materials value is - 0.24, 5. financials has -0.28, 8. telecommunications -0.3 and category 10; utilities has a coefficient of 0,34. All significant categories have a lower return compared to health care companies, with very similar numbers, besides utilities which are having a higher return.
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category 6; consumer services, 7; oil and gas and 9; technology has respectively coefficients of -0.14%, -0.07% and 0.068%. Nonetheless, these numbers cannot certainly explain a relationship since the p-values are not significant.
6.3 Multiple Regression Model of Risk (OLS Robust)
From table 14, an R-square of 10.85% can be observed. This is higher than the previous regression model with stock return as the dependent variable. It is however somewhat lower than the R-square for the regression model with ROA as the dependent variable. The R-square is relatively low, which indicates that there are many other variables that need to be included in order to properly explain the relationship with risk.
Table 14. Multiple Regression Model Risk
6.3.1 Risk and AI
The p-value for the AI coefficient in table 14 has a value of 0, which means that the null hypothesis can be rejected on a 95% confidence level. The coefficient has a value of 0.6%, which means that companies using AI have a higher risk compared to companies not using AI. The mean Risk for companies not using AI is 3.899%. The coefficient value lies between 0.35% and 0.84% on a 95% confidence interval.
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6.3.2 Risk and Market Capitalization
In table 14, it can be seen that the p-value for the market capitalization coefficient has a value of 0, and therefore a significant relationship with SD can be confirmed on a 95% confidence level. The market capitalization coefficient is -1.86e-14, which means that SD has a small negative relationship with size, and that risk decreases when size increases. One example of writing this in larger values is if the size would decrease by 10bn SEK, the risk would increase by 0.000186%. The value of the coefficient lies between -2.55e- 14 and -1.17e-14 on a 95% confidence interval.
6.3.3 Risk and Country
The reference category suited best for this model was Sweden. The p-values for Denmark, Finland and Iceland was 0.012, 0.02 and 0.002 respectively. This intends that all the coefficients are significant on a 95% confidence level. The coefficients are -0.29, -0.32 and -0.85 and the mean risk of the reference variable Sweden is 4.07%. This means that Swedish companies have the highest risk, then Denmark, third place is Finland and Iceland has the lowest risk.
6.3.4 Risk and Industry
For this dummy variable, category 1; industrials was suited best as a reference category. The p-value for category 3; basic materials 0.011, 4; health care 0.04, and for 5; financials 0.006. All of these p-values are under 0.05 and significant on a 95% confidence level. The p-values for category 2; consumer goods is 0.094, for 6; consumer services is 0.096, 7; oil and gas is 0.064, 8; telecommunications is 0.978 and 10; utilities 0.148. These p values are above 0.05 and therefore no significant relationship can be concluded on a 95% confidence level. consumer goods, consumer services and Oil and gas is however not too far from 0.05, and the null hypothesis on a 90% confidence level can be rejected and a significant relationship with SD can be confirmed. The mean of risk for the reference category industrials is 4%. The coefficient for consumer goods is -0.28% which shows that the risk is lower compared to industrial For basic materials, the coefficient is 0.55%, meaning that the risk in this category is higher compared to industrial companies. For health care the coefficient is 0.45%, for financials -0.37%, consumer services 0.48%, oil and gas 0.64%. The insignificant coefficients for the industries telecommunications, technology and utilities have respectively numbers of -0.35%, -0.1% and -0.47%.
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6.4 Hypothesis Summary
In table 15, the results of each statistical null hypothesis is presented with respective z- score and if they are rejected or accepted on a 0.05 significance level.
Table 15. Hypothesis Testing
6.5 The Truth Criteria
The researchers must consider the truth criteria in order to enhance the rigor, or quality of the study, as well as the credibility of the findings. When doing quantitative studies, such as this study in particular, the truth criteria consists of validity, reliability (Heale & Twycross, 2015, p. 66 ; Saunders et al., 2009, p. 156) and generalizability (Saunders et al., 2009, p. 158).
6.5.1 Validity
Validity can be divided into internal validity and external validity (Bryman, 2012, p. 47), whereas external validity also refers to generalizability (Saunders et al., 2009, p. 158). Internal validity concerns how accurately a measurement used in research is actually explaining what it is supposed to (Bryman & Bell, 2011, p. 159). The researcher has to consider for example if the relationship studies is a causal relationship, or if there are other variables affecting the results (Saunders et al., 2009, p. 157). Heale & Twycross (2015, p. 66) provides a practical example where the validity criterion is breached. Imagine a healthcare study that is supposed to measure depression, but in reality measures anxiety. Because something else is measured than depression, the study is not valid (Heale & Twycross, 2015, p. 66). There are several factors that have to be taken into consideration in regard to validity when concluding the research findings. First of all, history has to be regarded since Saunders et al. (2009, p. 157) explains how factors such as product recalls from manufacturers that takes place after a study of product quality, might be misleading and cause the findings to be invalid. Secondly, if the research subjects believe that the results will impact them in some way, the results will be invalid to some degree since the subjects might act differently. In the event of a case study similar
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to the previous example, the results might also be invalid due to other factors such as different instructions or other events taking place at the time of the study, making the results invalid. The validity can also be affected by research participants dropping out of the study before completion (Saunders et al., 2009, p. 157). Lastly, a study might have validity issues if there is an uncertainty of the direction of the causation. For example, there might be a relationship between two variables. However, the results might be unclear whether variable A affects variable B, or the other way around (Saunders et al., 2009, p. 158). Relating to this study, there is no risk for validity problems as those explained by (Saunders et al., 2009, p. 157-158). The observations will not change due to time, since historical data is collected. Furthermore, since this study is not handling research subjects, problems such as outside forces affecting the subjects will not be an issue. While it is possible that financial performance as an independent variable would have a relationship with AI usage, it is not something that is explored in this research, which is clear from the regression model. There should therefore not be any issues regarding causality. Furthermore, Studenmund. (2014, p. 50) gives some examples of how one can make sure that the results are valid in quantitative research. For example, by making sure that the equation for the regression model is sound and includes all important variables that should be studied. If one is to study financial performance for example, it is of importance to measure the variables of financial performance that are the most optimal for the study. In this case, three different variables of performance are utilized in three equations in order to enhance the validity. Furthermore, the researcher should also use the best estimator for the particular equation, which in the case of this study is the OLS.
External validity, or generalizability, concerns whether the research results can be generalized in other settings (Saunders et al., 2009, p. 158) One example from Saunders et al. (2009, p. 158), is if results concluded from studying one organization can be applicable on similar organizations. Relating to this study, the results from Nasdaq OMX Nordic can certainly be generalized for the Nordic market, as this stock exchange comprises a vast variety of large Nordic companies in various industries. However, it is unclear whether the study could represent financial performance effects due to AI usage in other geographical regions. As previously explained, the Nordic countries are in the forefront of technology implementation, which questions if the results could be generalizable in less developed economies. Additionally, it might not even be generalizable in western European countries, as the effects of AI are predicted to be different in this region regarding for example the unemployment rate. This is of no concern however, since the purpose is to study specifically the Nordic market.
6.5.2 Reliability
Whether a study’s results are reliable or not depends on the stability of the measures that are used. Stable measurements are defined as those who allow the study to be conducted repeatedly with the same consistent outcomes (Bryman, 2012, p. 46). In addition to producing the same results on different occasions, a study is deemed reliable if different observers would yield the same observations as the original researcher. Furthermore, a reliable study should have a high degree of transparency regarding the interpretation of the data (Saunders et al., 2009, p. 156). Robson (2002, cited in Saunders et al., 2009, p. 156) further explains four threats relating to reliability that are good to consider in order to assess the reliability of one’s own study. Firstly, there is the participant error, which
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means that research subjects might act differently repenting on for example the time of the week they are answering a survey. The solution to this is to perform the study during a neutral time, such as the middle of a week when exploring something such as employee enthusiasm. Secondly, there might be a subject bias in studies including interviews, where the subjects might feel pressured and afraid of answering truthfully unless they are assured anonymity (Saunders et al., 2009, p. 156). Saunders et al. (2009, p. 157) further explains that observer errors might occur due to for example an improperly structured interview schedule with more than one interviewer possessing different ways of asking questions. Lastly, there might be an observer bias in the previously mentioned situation if the different observers interpret the replies of the interviewees differently (Saunders et al., 2009, p. 157).
Relating to this study, there should be no problem for anyone to achieve the same results repeatedly on different occasions, as long as the same data is used. The data regarding financial performance is as previously mentioned collected from a database, where the data is the same for anyone collecting it as long as the same companies, variables and years are screened for. The data regarding AI usage should also technically be the same. However, there might have been some observer errors since there are two authors of this thesis and some material might have been missed when searching for information regarding companies AI usage. Nonetheless, this error would most likely be insignificant for it to cause the study to be unreliable. Furthermore, the discussion and analysis sections of this thesis are transparent and include in-depth explanations of how sense was made from the data, which causes a high degree of reliability. Regarding the threats of participant errors and subject bias, they should not be a problem for this study’s reliability since no research subjects are included in the study. Similarly to the observer error however, there might be some degree of observer bias. This is because the two authors might have interpreted the information regarding AI usage differently due to problems regarding for example translation errors. This would be very insignificant regardless and unlikely to cause any reliability problems.
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7. Analysis and Discussion
This chapter will include a discussion and analysis of the results of the study, investigating the connections to the relevant theories and previous studies on artificial intelligence and financial performance. Additionally, possible explanations of the results will be included. This will address the research gap and answer the research question.
7.1 Financial Performance and AI Usage
The foundation behind the hypothesis that implementation of AI has a positive impact on financial performance in the Nordic companies, builds on previous research in the area of AI and its contribution to economic benefits in businesses. This in addition to the fact that the Nordic countries have been in the forefront of technological utilization and involvement in technological companies. As previously explained, the economical benefits such as increased efficiency and the ability to cut costs, have been shown to be connected to financial performance through increased profitability (Fatihudin et al., 2018, p. 554). Such economical benefits have been seen in a wide range of sectors, including everything from the cost saving feature of avoiding to launch unsuccessful food products (Soltani-Fesaghandis & Pooya, 2018, p. 848), to the effectivization of the financial sector with AI serving as economical advisors as explained by Wheeler (2020, p. 66). Thus in theory, utilization of AI should technically result in a higher financial performance. Furthermore, AI has many times been referred to as a disruptive innovation (Wheeler, 2020, p. 66), having the ability of utilizing improvement, changing and creating new markets and patterns of competition as explained by the disruption theory by Christensen (1997, p. 218). Additionally, increased performance and shareholder value by AI usage can be argued for by the resource-based theory as it is defined by Barney & Clark (2007), since AI is only booming as of recently and can thus be adapted in rare and unique ways leading to a sustained competitive advantage. From a long-term perspective, the dynamic capabilities framework can also be used to explain a possible long-term competitive advantage by the utilization of AI (Cavusgil et al., 2007, p. 164).
Although more controversial, AI usage is also serving stakeholders’ interest. For example by emphasizing patients health in the healthcare sector (Garbuio & Lin, 2019), as well as improving the quality of life as seen by autonomous cars (Soegoto et al., 2019, p. 5), and emphasizing sustainability through its implementation in smart-grids of the energy sector (Ahat et al., 2013, p. 196 ; Ramchurn et al., 2012, p. 88). However, depending on whether one is having an optimistic or a doomsayer’s perspective regarding its impact on the workforce, AI might cause negative societal effects such as increased unemployment (Frank et al., 2019).
Three regression models with the dependent variables ROA, stock return and risk were done in order to include financial performance perspectives of internal performance, stock market returns and risk. AI implementation was expected to provide companies with better financial performance due to its economical benefits. Therefore, it was expected that ROA and stock return would have a positive relationship with AI usage. Additionally, as new investments are predicted to increase risk, it was expected that AI usage would have a positive relationship with volatility as measured by the standard deviation of stock returns. The following sections discuss and analyze the empirical results of each regression model and the respective relationship with AI, connecting it to the summarized
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theoretical framework and the respective theories.
7.1.1 ROA
The regression model of ROA provided a coefficient of -2.3%, which intends that companies that have implemented AI have a lower ROA compared to those who have not implemented AI. This means that investing in AI technology does not follow the shareholders’ interest of value maximization in the short run, while considering internal financial performance as measured by ROA. This was the opposite result than what was expected from the researchers, since it contradicts the idea of economical benefits resulting in an increased financial performance. However, this might be a result from the fact that the development of the technology is still in an early phase and only booming as of recently. Furthermore, as seen in the percentage of companies using AI in figure 12 from chapter 5, as early as in 2015, there were only 1% of the observations who had implemented AI, compared to 47% in 2019. It might be possible that it takes time before the AI investments finally pays off for the businesses, and a few years of using it might not simply be enough. These results thus only provide an insight of AI and its impact on financial performance in a short time horizon. It is possible that if the same study were performed in 10 years from now starting with observations from 2015, the results would differ dramatically due to businesses being more adapted to the technology. However, while it opposes the idea of disruptive technologies causing increased financial performance in the short run, there are many takeaways from the dynamic capabilities theory, since it emphasizes how companies need to adapt and be ready for change to achieve a long-term competitive advantage. Perhaps companies’ stakeholders such as employees and customers need to adapt to the new technology in order to achieve a full transition of relying on technology in both work and their everyday life. While early adopters might acquire many advantages of AI, all companies need to combine the new technology with the right talent and managerial strategies (Hartley and Sawaya, p. 714, 2019; He et al. 2018, cited in Wheeler, p. 69, 2020). Therefore, which companies will succeed and take advantage of the benefits, as well as the strengths and benefits it will bring is hard to say at this time. Moreover, while it is certain that some companies are utilizing AI in a rare and unique way, such as innovative usage in the healthcare sector, it might need more time to see results. This makes it reasonable to combine both having the dynamic capabilities and the resource-based view in order to assess the financial performance advantages of new technologies, which is also argued for by Cavusgil et al. (2007, p. 164).
In addition, during the data collection of companies AI usage, it was found that many companies had invested in R&D in the area of AI before implementing the technology. Additionally, many of the companies who had not implemented AI were still investing in research and had future plans of implementation (see appendix 1). This further strengthens the argument that it might take time for companies to see a financial pay-off from using AI technology. Furthermore, the studies by Matt et al. (2015) and Tekic and Koroteev (2019) presented information about the many challenges of AI implementation, such as managerial and operational which might also be early on focus areas to overcome in companies. Due to these reasons, it might be that investment and organizational changes have rather decreased the internal performance from large investments and increased operational costs. It might be that these new contributions still have not been established and paid off, but it might be more visible in the near future.
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Furthermore, since the coefficient of ROA is weak, it also explains that companies who have implemented AI and those who have not implemented AI are having very similar financial performances in terms of ROA in this early stage. As mentioned, many of the companies that have not implemented AI have still invested resources in research and development in the area. These investment costs may have similar internal financial effects, since it has been a cost. But due to the fact that they have not reached the implementation stage, no profits have been achieved. This also applies for companies that have a long history of researching and developing AI, but have implemented it just as of recently.
7.1.2 Stock Return
The regression model measuring Return provided a coefficient of -0.014%, which also shows that companies who have implemented AI have had a lower stock return compared to companies who have not implemented AI. Similar to the results regarding internal financial performance, this is contradictory from what the literature suggests and what was expected by the researchers. This shows that AI also has a negative impact on firms’ market financial performance, as seen from an investor’s perspective. However, since internal performance might be reflected in the stock price of a company, these results are reasonable considering the negative relationship between ROA and AI usage. Moreover, this is in line with the arguments made by (Irfani, 2014, p. 105), suggesting that stock prices are reflected by indicators such as ROA. Since implementations of AI have been made during the recent five years, it might have resulted in lower ROA and stock returns. But it might be that these investments will lead to higher future returns, and therefore higher positive ROA and stock return in the prospective long run. However, investments in R&D, as well as the implementation of AI should theoretically lead to increased future performance and payoffs in future cash flows, which would be expected to be reflected in increased stock prices and stock returns. This is also argued for by the dynamic capability’s framework, since it emphasizes the adaptation to technology to achieve a future competitive advantage. Since this analysis predicts that AI will pay off in relation to financial performance in the long run, it would be reasonable to assume that it could be a valuable investment today.
However, the relationship is very weak and there is barely any difference between companies that have implemented AI compared to those who have not. As explained earlier, many of the companies that were found not using AI, have still invested in AI during many years. The stock return between the two categories might not differ largely, since stock investors might see similar potentials, and no large differences in implementations of AI and investments in AI. Additionally, there is a huge variety between the way’s companies have implemented AI in their business processes. For example, the use of AI in self-driving cars might be revolutionary, while chatbots might not have as large economical contributions.
7.1.3 Risk
The regression model where risk was the dependent variable provided an AI coefficient of 0.058%, meaning that companies investing in AI have higher risk. This is in line with the expectations based on various studies presented in the theoretical framework (Floridi et al, 2018 ; King et al. 2018 ; Matt et al. 2015; Frank et al., 2019 ; Tekic & Koroteev,
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2019). These formulated that investments in new technologies can be risky, the success of a company depends on many challenges such as the implementation process as well as external and uncontrollable factors. Therefore, the volatility on the stock price was expected to be higher in companies investing in AI.
As stated by Chang and Doong (2001, p. 307) lower returns can lead to more uncertainty and volatility in stock prices. The observed higher risk might be an outcome of the lower ROA and stock returns. On the other hand, the results can as well be seen as contradictory due to the argument that risk and return is supposed to have a positive relationship, due to investors demanding higher compensation for taking on more risk (Chiang & Doong, 2001, p. 307). The results indicate that investments in companies utilizing AI have a slightly higher risk but a lower performance in terms of stock return and ROA. As previously discussed, it is however predicted that AI implementation might be profitable in the long run. Therefore, investing in companies using AI might suit investors who are willing to take risk and be ready to hold these stocks for a long term.
7.2 Analysis of The Extraneous Variables
7.2.1 Size
Size was included in all regression models as an extraneous variable in order to eliminate potential bias. The findings of the relationship between size and the dependent variables will be analyzed even though they do not specifically contribute to the research question.
As mentioned previously, it was expected that size and financial performance would have a positive relationship. This argument is supported by the resource-based theory, meaning that superior performance and competitive advantage is dependent on company resources, and that large companies are possessing more resources (Barney & Clark, 2007, p. 17). ROA had a very small positive correlation with size, showing that there is a small relationship between the variables. This confirms the expectations of the researchers, albeit to a small degree. The weak coefficient shows that companies in the population have pretty similar ROA, but larger companies are having a slightly better performance in terms of ROA. In the stock return regression, the coefficient for size was insignificant. This shows the size of the listed Nordic companies have no relationship with stock return. Furthermore, company size and risk showed a small negative correlation, meaning that larger companies tend to have lower risk. Therefore, the previous connections made in this thesis that large companies with more resources have a more stable position can be confirmed. The small negative coefficients can be a result of all companies being large and well established, therefore having quite similar attributes.
7.2.2 Country
The preconceptions about the Nordic companies was that they operated in very similar market climates, thus being similar and comparable. Country was however included as an extraneous variable in order to take away potential bias from the different countries. The results showed that there is a significant relationship between countries and ROA. Finland has slightly lower return than other countries who had around 2% higher ROAs. However, it seems like the ROA among the countries were similar. No significant relationship was found in the stock return regression, showing that there are no identified
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differences among the Nordic countries as in relation to market performance. In the regression model where risk was the dependent variable, Sweden was found to have highest risk, although it was very weak. In general, it can be seen that there is only a minor difference between the Nordic countries regarding these financial performance measures. This strengthens the arguments previously made of the ability to compare these companies as a single group.
7.2.3 Industry
The industry dummy variable was as well included as an extraneous variable. It is interesting to see whether financial performance has differed among companies in different industries, even though it cannot provide any answers in relation to AI implementation.
From these results, it can be concluded that consumer services have had the highest performance in terms of ROA. For the categories; basic materials, oil and gas and utilities no relationship with ROA could be identified. The stock returns do not differ much, but it could be seen that telecommunications have had the lowest stock returns, while utilities have the highest. In the risk regression, it could be seen that basic materials have had the highest volatility among the industries, while the financial sector had the lowest risk. In general, however, risk levels were not found to differ a lot between the industries.
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8. Conclusions and Future Research
This concluding chapter will present the conclusions that can be drawn from the analysis of the study results. Afterwards, the practical and theoretical contributions will be discussed, including its societal and ethical implications. Lastly, the chapter will discuss the limitations of the studies, and possible topics for future research.
8.1 Conclusions
The idea behind this thesis stemmed from the fact that AI has boomed in recent years and that Nordic companies are seen as being in the forefront of implementing new technology. This together with literature showing possible economic contributions of AI opened up the interest of investigating whether AI implementation in Nordic companies have led to an increased financial performance. During the data collection, it was found that companies implementing AI had increased dramatically during the past five years, with an increase of 1% of the companies using AI in 2015, to 47% using AI in 2019. This further showed that AI is in a beginning phase while also being very relevant in today's business. The purpose of the study was to investigate whether financial performance has increased by implementing AI by looking at the three variables ROA, stock return and SD of stock return by conducting three regression models. These variables were included in order to get a perspective of both the internal performance, market performance and risk as measured by volatility. The extraneous variables size, country and industry was included in the respective regression analysis in order to eliminate potential bias. As previous studies have shown that AI can contribute to economic benefits, it was expected that companies using AI would have a higher financial performance. As AI is a new technology and an unexplored area, higher risk was expected as an outcome. The results showed that companies that have implemented AI have a lower ROA by -2.33%, a lower stock return by -0.14% and a higher risk by 0.6%. The results of ROA and stock return was the opposite of what was as expected. However, this outcome can be explained by the fact that AI is in an early stage and that there are many challenges and costs to companies implementing AI. Higher investment cost as well as higher operational costs can explain that AI implementations have still not become profitable, which might explain the weaker internal performance as well as the lower stock price observed. Future cash flow is supposed to be reflected in the stock price and since AI is expected to at least create value in the future, this result was surprising. The risk for companies using AI was higher as expected, but this is contradictory with the result of lower returns, since higher risk normally demands a higher return. Additionally, the relationships found were not very strong and it might be that as of now, there is not a huge difference between companies using AI and companies who do not. During the data collection, it was also found that many companies that have not implemented AI have still been investing in research and development for many years, which may be one reason behind these weak relationships. The extraneous variables did also show weak relationships, meaning that the large Nordic companies have slightly similar attributes.
Concluding this study, investing in AI can be seen as risky, both based on the risk variable, as well as the fact that companies using AI are currently not performing better than companies not using AI. There is not any guarantee that these companies will perform better in the future, since there are many factors the success can depend on. However, referring back to the fact that the increase of companies that have implemented
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AI has gone from 1% in 2015 to 47% in 2019, shows that it is widely believed that AI eventually will improve financial performance. It is predicted that AI will contribute to value creation, as well as competitive advantage in the future, which means that investing in AI might be suitable for both companies and investors in the long run. This is however only predictions, which by the time of conducting this study cannot be confirmed. Based on the five past AI has not contributed to increased financial performance in the Nordics. Additionally, the expectations that AI would increase financial performance was a somewhat biased and qualitative preconception and could not be seen statistically.
8.2 Contributions and Implications
8.2.1 Practical Contributions, Societal and Ethical Implications
The findings of this study have several practical implications for firms’ shareholders and stakeholders. For example, since both internal and market performance were shown to be negatively affected by AI implementation, this would lead to investors being more hesitant to invest in companies utilizing AI technology, not maximizing shareholder value. This is at least true in a short time horizon, since the results cannot explain AI’s effect on financial performance in the long run. However as of now, the analysis would certainly point in the direction that AI is a long-term investment rather than a short term one. This is especially true considering theories such as the dynamic capabilities theory which emphasizes long term competitive advantage from technological adaptation. Moreover, the shown negative relationship between AI usage and financial performance should technically make senior executives unwilling to make further resource expenditures into the technology. From the stakeholders’ point of view, this would have both positive and negative impacts. For example, less implementation of AI would benefit employees and communities regarding the unemployment rates, since people would be less likely to be replaced by machines in their work. The fact that AI can be used as a replacement of human workers is an ethical issue, and if fewer workers are replaced by AI, the negative psychological effects of being unemployed will be limited. However, this is mostly true from the doomsayer’s perspective of AI in relation to future unemployment. Depending on one's perspective, AI might even create new jobs, which thus would cause these results to have the opposite effects. Furthermore, a decrease in AI development could benefit governments, since they would not have to spend as many resources on countering cyber risks that stems from AI. It is worth considering the ethical benefits that comes with a decreased initiative to develop AI technology, for example the possibility of less cyber weapons, as well as the increased stability and security in society that brings. A decrease in AI usage overall could also lower the rate of AI crimes, resulting in a safer online environment with less fraudulent and illegal activities. Furthermore, less dependence of AI would limit ethical dilemmas such as to what degree AI should be used in decision making, since there is an absence of human responsibility with a complete reliance on AI when making decisions. Additionally, it could have positive societal effects such as making people less reliant on technology, referring to the Hikikomori generation and risk of humans becoming too addicted to AI technology as explained by Turchin and Denkenberger (2018, p. 153). Nonetheless, it is unknown to what extent AI development in that area would be limited due to the results showing how Nordic businesses are less profitable from investing in AI.
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However, stakeholders’ and society would also be negatively affected by a decreased AI development. The usage of AI in the healthcare industry is revolutionary, and improves efficiency which helps save lives, as well as improving customers’ quality of life. This is also true for the automotive industry, where less investments in self-driving cars neglects beneficial factors such as less road accidents and lower emissions stemming from more effective driving. Lower emissions are also an indirect benefit of using AI in the energy sector, due to its involvement in smart-grids, and less AI implementation would thus have a negative impact on sustainability considering the environmental factor. In addition, one has to consider the advantages of AI in regard to its usefulness for individuals to pursue self-realization. As discussed in chapter 2, AI can be an aid for people, making it easier to focus on things such as individual goals and interest. If AI is used and developed to a lesser extent, the individual benefits of AI would be directly counteracted which is negative for the society. To summarize, AI has both pros and cons regarding social and ethical aspects, considering its impact on firms’ stakeholders. Results indicating a lower performance due to AI implementation, promotes less AI usage, leading to societal effects in various ways.
8.2.2 Theoretical Contributions
Regarding the theoretical contributions, the results of this study shows that from the shareholder theory’s perspective, firms’ social responsibility and value maximization in regard to AI usage is to not utilize it in the business operations. The shareholder and stakeholder view thus share some commonalities, since a lack of AI implementation both maximize shareholder’s value, as well as the value for some stakeholder such as the employees, although somewhat controversial as previously explained. However, a lack of AI implementation would not benefit all stakeholders, nor would continue AI implementation. This is in line with the argument of Freeman (2010, p. 5), since conflicts between stakeholder groups are not uncommon. However, regarding AI implementation, it is unclear as to which action would be beneficial to most stakeholder groups. This thesis thus leans toward the shareholder view of value maximization, at least in the short run, since not implementing AI is the apparent way of maximizing shareholder value in Nordic companies in a short time horizon.
Additionally, the extraneous variables used in this study have brought additional information that can be beneficial to different stakeholders. It was found that there are some differences among companies regarding size, countries, and industries. These findings can however contribute with information that can be beneficial as for instance investors. Even though there were small differences between companies, these findings can be informative and taken into account in investment strategies in order to generate higher profits.
Connecting to the theories concerning competitive advantage as a result of properly adapting to and utilizing disruptive technologies, it is clear that a combination of the resource-based view and the dynamic capabilities theory is the way to go in regard to AI implementation. This is because the dynamic capabilities theory highlights the importance of adapting to technological change to stay competitive in the long run. While the results show that AI negatively impacts financial performance in the short run, it is reasonable to assume that it is an important technological tool to stay competitive in the long run, considering the economic benefits it brings. It is likely that companies need time to adapt and properly manage its resources before the technology starts to pay off both in
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regard to internal and market performance. However, it is still certainly a risk and nothing is known for certain, which is highlighted by the positive relationship between volatility and AI usage. The argument of combining these two theories for assessing the financial performance advantages of new technologies is thus aligned with the thoughts of Cavusgil et al. (2007, p. 164). However, it is certainly no doubt that AI is a disruptive technology, which can be seen since so many companies are following suit and adapting it into their business operations. The conclusions that can be drawn from this thesis share some standpoints with the disruptive theory by Christensen (1997). However, as previously mentioned it is still in an early phase and since the results show a short term negative impact of AI on financial performance, it might be the case that these types of disruptive innovations take time before they really start to pay off and provide a competitive advantage in the companies.
8.3 Limitations and Future Research
A limitation of this study can be seen as the possibility that companies that are categorized as using AI and not using AI might have similarities which weakens the results. This might be due to the fact that companies that have invested in AI and implemented AI both have R&D costs, which can result in decreased ROA and stock return and higher risk. Additionally, companies that have implemented AI are in an early phase and investment have not visibly resulted in higher profits by the time of writing, which as well can result in more similarities among the companies in the two groups. A study where categories were grouped based on other and more attributes would maybe result in a more clear and interpretative result.
It would also be interesting to conclude a similar study including R&D as an independent variable in the regressions. This could give an insight into how much companies have potentially spent on researching AI, which would help to remove noise related to costs which would impact profitability and thus affecting the dependent variables measuring financial performance. Conducting this study at a more advanced and explanatory level could be to measure AI numerically rather than categorically. This is due to the fact that companies do have different varieties of AI usage. For example, the use of AI in self- driving cars might be revolutionary, while chatbots might not have as large economical contributions.
The findings of this study only give a short-term perspective of the financial effects of AI utilization, it is suggested that a similar study is performed for example in the next decade. This is because in the 2030’s, it is possible that AI usage has started to pay off in regard to financial performance, which is further strengthened by the estimations of AI induced growth in 2035 as illustrated in figure 1. Additionally, it would be of interest to conduct a future study of how AI impacts financial performance in a specific industry. This is because as of now, the study’s results do not really tell the difference regarding the financial impact of AI in the different industries, but rather how the general financial performance differs between them. It would be interesting to properly investigate for example the healthcare industry or the energy industry, since these industries are known to have positive societal and environmental impacts as a result of using AI. When researching these industries, it could also be intriguing to perform a different study design, perhaps by investigating the subjective and societal value creation of AI, rather than financial performance.
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Lastly, to elaborate on the knowledge of AI within the field of finance, it could be compelling to measure AI usage with another variable. An example would be to perform another analytical research measuring the relationship between AI usage and senior executive compensation. This could be performed by doing two regressions, including AI usage as both an independent and a dependent variable in order to see if there is a causal relation between the two factors. Such research would incorporate the agency theory and further develop on the shareholder and stakeholder perspectives regarding AI.
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Appendix 1
Company Industry AI Currency
Maersk Industrials 2018 DKK
The company has published news about investments in artificial intelligence in early 2018. News about this can be found on external web pages online as well. There is no information disclosed about AI in the latest annual reports The maritime executive (2018.11.10) Maersk Invest in Artificial intelligence truck flow system. https://www.maritime-executive.com/article/maersk-invests-in-artificial-intelligence-truck-flow-system https://www.porttechnology.org/news/maersk_invests_in_ai_start_up/ [Retrieved 2020.04.14] Maersk invest in Load smarts AI truck flow system. The system enables shippers to book a truck manually or integrate a server-to- server booking system that requires no human intervention. The company claims that shippers have been able to reduce their spot exposure by 50 percent after integrating with Load smart, while reducing procurement execution time by up to 90 percent. Maersk (2018.01.16) Maersk and IBM to form joint venture applying blockchain to improve global trade and digitize supply chains. https://www.maersk.com/news/2018/06/29/maersk-and-ibm-to-form-joint-venture [Retrieved 2020.04.14] Info about investment in Artificial Intelligence. 2021 Schroder, P 2018.01.16). Optimizing revenue revenue opportunities in the product tanker shipping industry. 2021 AI. https://2021.ai/maersk-optimizes-revenue-opportunities-ai/ [Retrieved 2020.04.14] Collaborates with AI2021 Maerks (2019). Annual Report 2019. Available via: https://investor.maersk.com/static-files/984a2b93-0035-40d3-9cae- 77161c9a36e0 Nothing about AI disclosed in the company's latest annual report
AAK Consumer goods No AI SEK
No information about AI on AKK´s web page or in the news. No information about AI in their latest published annual report (2018) or the previous ones. AKK (2018). Annual Report 2018. Available via: https://ebooks.exakta.se/aak/2019/annual_report_2018/pubData/source/AAK_Annual_Report_2018_Ebook.pdf Nothing in their business development or innovation reports. https://www.aak.com Nothing in their business development or innovation reports nor in the company webpage. Company fact sheet of Nasdaq/Morningstar- no info of AI. No online search is AAK AB artificial intelligence from google.
ABB ltd. Industrials 2015 SEK
According to the ABB annual report they started investments in AI in 2015, before that year there is no mention of AI in their own reports/web pages or in the news that was available in the internet. After the year of 2015, there is articles and news about ABB´s AI investments and strategies and the usage has increased. This information can also be found in their annual reports. ABB (2019.11.06). ABB uses AI to revolutionize energy management. https://new.abb.com/news/detail/41194/abb-uses-ai-to-revolutionize-energy-management [Retrieved 2020.04.14] (Are investing in an energy forecasting app in order to predict usage peaks in power, to find strategies preventing them). Rusch, B (2018.11.28). ABB launches start-up accelerator for industrial AI. Hannover messe. Available via: https://www.hannovermesse.de/en/news/news-articles/abb-launches-start-up-accelerator-for-industrial-ai [Retrieved 2020.04.14] (ABB technology ventures (ATV) is a strategic venture capital unit of ABB. ATV invites startups from industrial automation, robotics and power grids to smart cities and buildings). ABB (2017.04.25). ABB och IBM samarbetar för att få lösningar för industriell artificiell intelligens. Available via: https://computersweden.idg.se/2.2683/1.681170/abb-ibm-samarbete-ai [Retrieved 2020.04.14] Begins to co. work with IBM with industry solutions of AI. ABB (2017) Annual report 2017. https://library.e.abb.com/public/7fbb3c078f10489c92a03eb5f3d89751/ABB-Group-Annual- Report-2017-English.pdf?x-sign=Apy1H6MtCaSQUXvZOTjA8JhI4RB97LiJgKJmYhS7iFsKONxOs6456DpSpY08WqU6 (ABB Ability was launched in 2017, which is a digital offering, contributing to develop and contribute to the 4th digital revolution). ABB (2016). Annual report. https://library.e.abb.com/public/e651ef240985432988577a68423b256b/abb-group-annual-report- 2016-english.pdf?x-sign=vqWEqE0qodhBs5/6/m4zTlpvrxNBqvOuxZ4jXPgLQntLR8MGTdSTiTHi9rLY8ND0 In p. 7. ABB are disclosing that that they are using AI and machine learning. ABB (2015). Annual Report 2015. https://www07o.abb.com/docs/default-source/investor-center-docs/annual-report/annual-report- 2015/abb-group-annual-report-2015-english.pdf (Mentioning artificial intelligence 6 times. ABB are disclosing information that they have invested in AI and that this will be the next stage)
Addtech Industrials No AI SEK
93
The company is a mother company to 130 automotive companies in different niches, but the common denomination is technical products. There is no information available when searching for AI, but since their business idea is to be an owner of other companies - it is hard to judge from this search. https://www.addtech.se/fileadmin/user_upload/Capital_Markets_Day_190919.pdf Mentioning machine learning and artificial intelligence, p. 53. Addtech (2018/2019) Annual report. https://www.addtech.se/fileadmin/user_upload/Dokument/Årsredovisningar/Addtech_AArsredovisning_2018-2019.pdf Do not mentioning AI artificial intelligence nor machine learning.
AHLSTROM-MUNKSJÖ Basic materials No AI EUR OYJ
The company is in the fiber-product business and is about to implement some digital solutions to its business operations in 2020. The company does not seem to invest in AI/machine learning solutions this far. Ahlstrom-Munksjo (2019). Annual Report. https://www.ahlstrom-munksjo.com/globalassets/investors/reports-and-presentations/2019/financial-statements-release-2019.pdf Do not mention AI or machine learning. https://www.tekoalyaika.fi/en/companies/ahlstrom-munksjo-oyj/ Nothing. Evry (2020.02.21). Ahlström-Munksjö selects tieto EVRY as digital business renewal partner. https://www.evry.com/en/about-evry/media/press-releases/2020/february/ahlstrom-munksjo-selects-tietoevry-as-digital-business- renewal-partner/ Has started to invest in more digital solution in their corporation procedures latest year.
Alfa Laval Industrials 2018 SEK
Searching for information about Alfa Laval and the AI procedures, there is not much information available. The company is not in detail talking about artificial intelligence, even though they are investing in new technology and the “internet of things”. There is no information on their webpage, but according to one online news page, some of their products are driven by AI, and algorithmic machine learning. This article was published in 2018 and was news. In their “adaptive Fuel Line Bluebook, no information of AI is disclosed. Therefore, we draw the conclusions? that the company has used AI since 2018. Alfa Laval (2019). Annual Report. https://www.alfalaval.com/globalassets/documents/investors/swedish/arsredovisningar/arsredovisning-2019.pdf Mentions nothing of AI operations, but are mentioning that they are investing in new technologies. Alfa laval and Noda (2018.02.05). The heat is on: How artificial Intelligence is helping to ward off the Swedish winter chill. Available via: https://www.euroheat.org/knowledge-hub/heat-artificial-intelligence-helping-ward-off-swedish-winter-chill/ Alfa Laval IQ Heat controllers, managed by a self-learning algorithm, is keeping customers warm and dry. https://www.alfalaval.com/search/?query=artificial+intelligence&category=All Investing in technology processes, such as AI Alfa Laval (2017). The alfa Laval Adaptive Fuel Line Blue Book. Available via: https://www.alfalaval.com/globalassets/documents/products/separation/centrifugal-separators/separators/innovator/alfa-laval- adaptive-fuel-line- bluebook.pdf?_t_id=1B2M2Y8AsgTpgAmY7PhCfg%3d%3d&_t_q=artificial+intelligence&_t_tags=language%3aen%2csiteid%3 af6138c09-ef87-414f-874d- e8bcf6fd81b8&_t_ip=83.185.34.95&_t_hit.id=AlfaLaval_ContentTypes_MediaTypes_GenericMedia/_906dee82-7751-4073-98f6- a303a4844a4a&_t_hit.pos=3 Mentions nothing about AI, Machine learning https://www.sisp.se/start/stockholm-exergi-partnering-up-with-sentian-ai Mentioned that Ignite Sweden has cooperated with Alfa Laval and that a recent addition to the success is Airpower energy. There is no detailed information about this, but might mean that Alfa Laval have used AI.
ALK-Abelló Health Care No AI DKK
There is no information from the company's annual report, on their webpage, or from searching online that they are using artificial intelligence. ALK (2019) Annual Report. https://ir.alk.net/static-files/a434d369-cc20-4997-98f6-21a619692584
ALM brand Financials No AI DKK
There is no information online, on the company's web page or in their latest annual report that they are having AI in their operations. The search word was also typed in Danish. Ruskov, J (2018). Tre stærke forsikringsselskaber kæmper om at blive Årets Digitale Finansvirksomhed. Available via: https://finanswatch.dk/Finansnyt/Forsikring/article10484130.ece The bank has been winning a price for having good digital solutions for customers, but here only he bank Alka is mentioned when it comes to “kunstig intelligens” (AI in Danish) ALM Brand (2019) Annual Report.https://s22.q4cdn.com/306893677/files/doc_financials/2019/ar/Alm.-Brand-AS-2019.pdf
Ambu Health care No AI DKK
94
There is no information disclosed on Ambu´s website or in their annual report that they would be investing in AI. Neither any news about this. Ambu (2020). Ambu financial reports. Available via: https://www.ambu.com/corporate-info/investors/reports/reports-in-english No information about AI, Machine learning
Arion banki Financials 2018 ISK/SEK
According to some information that the bank has published they are using artificial intelligence in the lending process and to identify appropriate customer products from 2018. In the searching process the Icelandic word “gervigreind” was used as well, but did only got information on the English searches, Airon banki (2019). Annual Report https://arsskyrsla2019.arionbanki.is/english/ https://ml-eu.globenewswire.com/Resource/Download/6cf4908b-11b4-45ac-a9b0-33b41a58b6f3 In Q4 2019 machine learning has been used in the bank to identify appropriate products for customers (p.9). Arion bank (2018). Credit Card and Overdraft Limits http://wwwv2.arionbanki.is/library/skrar/Fyrirtaeki/Arion-Banki-nyskopun/White-papers-pdf/5_CreditCardOverdraftLimits.pdf The bank uses algorithms and AI in the bank's lending process.
Arjo B Health care 2018 SEK
Arjo was splitted from gretinge in the end of 2017. From 2018 the company has invested in R&D in AI and other technologies. Pharma boardroom (2019.10.01). Joacim Lindiff - CEO, Arjo https://pharmaboardroom.com/interviews/joacim-lindoff-ceo-arjo/ Written in 2019, Mentioning tthat they are already (from last year, 2018) heavily investing in new technology such as AI. Arjo (2019). Annual Report https://arjo.inpublix.com/2019/wp-content/uploads/sites/2/2020/04/ARJO_19_SE_HI.pdf No more info about artificial intelligence in their annual report Di (2017.12.12). Börspremiär för Getinges avknoppning Arjo. Available via: https://www.di.se/nyheter/borspremiar-for-getinges-avknoppning-arjo/ In the end of 2017, rjo was splitted from Geinge.
ASSA ABLOY Industrials 2016 SEK
Based on the news on Assa Abloy on page “futureLab” the company started investing in AI 2016. Simmons, C (2019.08.14). The thoughtful home. FutureLab, Assa Abloy. https://futurelab.assaabloy.com/en/making-artificial-intelligence-real/ Assa started to think about AI for around 3 years ago, meaning around 2016 Chapman, D (2019.12.05). Making artificial intelligence real. FutureLab, Assa Aboly. https://futurelab.assaabloy.com/en/the-thoughtful-home/ Saying that it was in 2016 Assa Abloy started investing in technologies as AI.
Astra Zeneca Medical Care 2017 SEK
There is nothing about AI mentioned in the 2016 annual report but in 2017 they are mentioning that they are using AI to transform medical chemistry. They are also mentioning that they have researched it since 2012. Since they are not mentioned in theri annual reports that they are using it in their operations before 2017, we assume that it is the year the company started using it. AstraZeneca (2020). Data science and artificial intelligence. Available via: https://www.astrazeneca.com/what-science-can- do/artificial-intelligence.html The company is investing much in R&D in AI. AstraZeneca (2018.11.29). Artificial intelligence: The next frontier in medical science. available via:https://www.astrazeneca.com/what-science-can-do/labtalk-blog/uncategorized/artificial-intelligence-the-next-frontier-in- medical-science29112018.html AztaZeneca (2017) Annual Report. Available via: https://www.astrazeneca.com/content/dam/az/Investor_Relations/annual-reports- homepage/AstraZeneca_AR_2017%20(1).pdf AI is used to transform our medicinal chemistry and they have researched since 2012.
Atlas Copco Industrial 2018 SEK
The company was developing in the beginning of 2018 and already using it in the beginning of 2019. Also talked about the benefits i 2017. Therefore, we assume that they implemented it in 2018. EFN( 2019.01.28) Atlas Copco hänger på aI tenden. https://www.efn.se/verkstad-industri/atlas-copco-hanger-pa-ai-trenden/ CEO telling that they are using AI. https://www.nyteknik.se/sponsrad/atlas-copco-utvecklar-innovativ-mjukvara-for-smarta-industriverktyg-6930405 “Atlas Copco is developing AI-technique”. Capgmini (2017). Turning AI into concrete value: The successful implementers tooldkit https://www.capgemini.com/wp-content/uploads/2017/09/artificial-intelligence-e28093-where-and-how-to-invest.pdf Quoting the CEO talking about how AI can contribute to revenue in many ways.
95
Atrium Ljungberg Financials No AI SEK
There is no information online or in their latest annual report of AI. Atrium Ljungberg (2019). Annual Report https://mb.cision.com/Main/1145/3049908/1203730.pdf
Attendo Health Care No AI SEK
There is no information online or in their latest annual report of AI. Attendo (2019). annual Report. https://www.attendo.com/495233/siteassets/attendo-com/dokument/annual-report/attendo- arsredovisning-och-hallbarhetsrapport-2019.pdf No information of AI or Machine Learning
Autoliv SDB Consumer Goods 2018 SEK
The company was researching in 2016 and is still researching to develop self-driving carts using AI in new launch of vehicle in 2018, therefore we assume that it was released in a product first in 2018. Autoliv (2018). MET liv 2.0. https://www.autoliv.com/sites/default/files/documents/ALV_Meet%20LIV_WhitePaper.pdf In 2018 they where using AI in some different areas of their vehicles. https://news.cision.com/autoliv/r/autoliv-research-to-launch-learning-intelligent-vehicle,c2153639 in 2016 research on AI was on. Autoliv (2019). Annual Report. https://www.autoliv.com/sites/default/files/Autoliv_Annual_Report_2019.pdf Do not mention AI in annual report.
Avanza Bank Holding Financials No AI SEK
The bank does not disclose any information about usage of AI. There is no news or information about this online. Avanza (2020). Rapporter. https://investors.avanza.se/ir/rapporter/arsredovisningar-och-delarsrapporter/
Axfood Consumer services 2018 SEK
In Ax foods 2018 annual report they mentioned for the first time that they are using AI in risk operations. They are an owner of Mat.se who also used AI in 2018. Axfood (2018.12.06). Mat.se förutspår framtiden med artificiell intelligens. https://www.axfood.com/globalassets/startsida/investerare/rapporter-och-presentationer/axfood_ar18_eng.pdf Are using AI in risk operations. AxFood (2020). Operational and strategic risks. https://www.axfood.com/investors/risks-and-risk- management/operational_and_strategic_risks/ Adopting AI to work with risks. https://www.axfood.se/media-och-opinion/pressmeddelanden/2018/12/mat.se-forutspar-framtiden-med-artificiell-intelligens/ Axfoods owns mat.se whose are using AI
Beijer REF AB Industrials No AI SEK
No information whether the company are using AI. In The annual report on 2018 and 2019, the company are mentioning that this will be a force in the digitalization, but not that they are using it. Beijer ref (2020). http://www.beijerref.com/sv/Foeretaget Are mentioning in Annual Report that it will become more common with AI when it comes to digitalization, but no information that the company are using AI
Betsson Consumer Service No AI SEK
Daughter company are using AI in chatbot. This is a holding company and there is no information whether the AB is using it. Betsson group (2019.04.26). Betsson group bets on Ada as its Official Automated Customer Experience Partner. https://www.betssongroup.com/betsson-group-bets-on-ada-as-its-official-automated-customer-experience-partner/ Using AI in chatbot Bettsson AB (2019). Annual Report. https://vp273.alertir.com/afw/files/press/betsson/202004063917-1.pdf No info about AI
BILL BillerudKorsnäs Basic Materials 2019 SEK
96
Are not mentioning AI in annual report but according to news they have partnered up with other companies in order to develop and also in 2019, they are suing it in analyzation systems.
Pelatrion (2020). Speeding up paper production with AI. https://peltarion.com/solutions/cases/ai-and-paper-production enlisted company with AI competence to implement it in processes - task automatization. ABB (2019). Digital center optimizes production. https://new.abb.com/pulp-paper/abb-in-pulp-and-paper/articles/latest/digital- center-optimizes-production-at-billerudkorsnäs-packaging-producer Collaborates with ABB in order to develop AI/technology. BilletunKorsnäs (2017.06.29) SEK3 million from Vinnova for leading-edge initiative. https://www.billerudkorsnas.com/media/news/2017/sek-3-million-from-vinnova-for-leading-edge-initiative Started project in developing AI. BillerudKorsnäs (2019). Annual Report. https://www.billerudkorsnas.com/globalassets/billerudkorsnas/investors/2019-one-pager/bk-asr19.pdf No info in Annual Report. Times (2019). AI sees the forest as well as the trees. https://www.timeslive.co.za/sunday-times/business/2019-05-12-ai-sees--the-forest-as-well-as--the-trees/ Implemented AI in analyzing system.
Boliden Basic Materials No AI SEK
Are investing in AI research but have not implemented it yet. But are seeing it happen in around 10 years in the form of self-driven vehicles. Goodman, S (2017.12.17) The robots are coming, and Sweden is fine. New York Times. https://www.nytimes.com/2017/12/27/business/the-robots-are-coming-and-sweden-is-fine.html Testing AI-self driving vehicles Boliden (2019). Annual Report. https://vp217.alertir.com/afw/files/press/boliden/202003107219-1.pdf “opens up possibilities”,
Bonava Financials No AI SEK
There is no information online or in annual reports. Bonava (2018). Annual Report. https://vp224.alertir.com/afw/files/press/bonava/202003106910-1.pdf Not mentioning AI
Bravida Holding Industrials No AI SEK
There is no information online or in annual reports. Bravida (2019). Annual Report. https://www.bravida.se/globalassets/9.-investors/financial-reports/swedish-reports/2019/2019-bravida-arsredovisning.pdf Not mentioning AI.
Cargotec ojy Industrials No AI EUR
Are currently researching and planning on using AI, see much opportunities, but has not yet implemented it. Are targeting more development of machine learning in 2020. Cargotec (2019.01.06) Smart regulation and active dialogue are needed to support the development of artificial intelligence. https://www.cargotec.com/en/smarter-better-together/blogs-and-articles/smart-regulation-and-active-dialogue-are-needed-to- support-the-development-of-artificial-intelligence/ CEO is supporting government in AI research - but do not mention that cargotec is using it in their supply chains. Cargotec (2019.04.02) Can startup collaboration help transform data sharing and usage? https://www.cargotec.com/en/smarter-better-together/blogs-and-articles/can-startup-collaboration-help-transform-data-sharing-and- usage/ Looking to collaborate with startups. Cargotec (2019). Annual Report. https://www.cargotec.com/491d6e/globalassets/files/investors/reports/2019/cargotec_annual_review_2019_web_final.pdf Are aiming to continue machine learning development (targets 2020).
Carlsberg Consumer goods No AI DKK
97
Investing in AI but have not started using it. Started a AI project in 2017, in 2019 it was still running. Could not find current information so probably it is still in process. Arthur, R (2017. 11.07). Carlsberg turns to artificial intelligence in “beer fingerprint” project. Beverage daily. https://www.beveragedaily.com/Article/2017/11/07/Carlsberg-turns-to-artificial-intelligence-in-Beer-Fingerprinting-project Researching AI in 2017. Ray, s (2018. 07.18) Van AI help brewers predict how new beer varieties will taste? Carlsberg says “probably”. Microsoft. https://news.microsoft.com/transform/can-ai-help-brewers-predict-how-new-beer-varieties-will-taste-carlsberg-says- probably/ Project is on in 2018, Carlsberg says that AI can probably be used to predict beer tastes. Carlsberg group (2019). Financial statements. https://www.carlsberggroup.com/media/35956/05_04022019_fy2019-financial-statement.pdf Testing machine learning in 2019 - not implemented.
Castellum Financials 2019 SEK
In 2019, the company was using AI. No information earlier than that is available. Castellum (2019). Castellum utilizes AI to maximize meeting-room efficiency at SCB. https://www.castellum.se/da/nyheder-presse/press-releases/2019/11/castellum-utilizes-ai-to-maximize-meeting-room-efficiency-at- statistics-sweden/ Using AI/ aiding customer with AI. Castellum (2019). Annual Report. https://annualreport.castellum.se/2019/en/operations-and-properties/innovation.html Using and developing AI.
CHR hansen holding Health Care No AI DKK
No information of AI available, but from searchin the danish work “kunstig intelligence”, it was available information that investments started in 2017, and was also in 2018, 2019. No other information available. Chr Hansen (2020) Reports. https://www.chr-hansen.com/en/investors/reports-and-presentations Nothing mentioned. Borsen (2017.07.31). Chr Hansen minsker madspild med machine learning. https://borsen.dk/sponsoreret/chr-hansen-mindsker-madspild-med-machine-learning New Project in “kunstig Intelligens”(AI). Fodevarewatch (2018.05.04). Kunstil intelligens giverfodevaruindustrien helt nye muligheter. https://fodevarewatch.dk/Fodevarer/article10572015.ece Heavily investing in AI.
Citycon Oyj Financials No AI EUR
No information about AI available online. From searching in with the fisnish words for AI “Tekoäly” no results was found either Citycon (2019). Annual report.https://www.citycon.com/sites/default/files/financial_review_2019.pdf Nothing about AI.
Coloplast Health care 2019 DKK
Are mentioning in 2019 that they are using AI. But there if no information before that year. Coloplast (2019.08.20) Meet the management. https://www.coloplast.com/Documents/Investor%20Relations/Presentations/2018- 19/Coloplast%20Meet%20the%20Management%202019%20Web.pdf Using AI.
Danske Bank Financials 2019 DKK
Took the first steps of AI in 2019 Danske Bank (2019). Smart human brains enables Danske´´ AI technology to predict IT breakdown. https://danskebank.com/news-and-insights/news-archive/news/2019/28022019 Has taken the first steps of machine learning Teradata (2019). Danske Bank: Innovating in Artificial Intelligence and Deep Learning to Detect Sophisticated Fraud and be the Most Trusted Bank in the Nordics https://www.teradata.se/Resources/Customer-Videos/Danske-Bank-Innovating-in-Artificial-Intelligence-and-Deep-Learning Using AI
Demant Health Care 2019 DKK
98
Searching for “kunstig intelligence” it is possible to find news about the usage of AI in 2019. Medwatch (2018) William demant klar med kunstig intelligence til horeapperater naste år. https://medwatch.dk/Medico___Rehab/article10996605.ece Lanuchin AI in application in January 2019. DI business (2019). Demant-boss: Livsnødvendigt at omfavne ny teknologi https://www.danskindustri.dk/di-business/arkiv/nyheder/2019/6/livsnodvendigt-at-omfavne-digital-teknologi/ Using AI in 2019.
DFDS Industrials 2019 DKK
Started using AI in 2019. DFDS (2019). Trends we follow in DFDS. https://www.dfds.com/en/about/media/news/trends-we-follow-in-dfds Tested AI for drones 2019. DJI(2020). LEVERAGING DRONE AUTOMATION FOR TERMINAL INSPECTIONS https://enterprise.dji.com/news/detail/drone-automation-for-terminal-inspections DFDS has seen efficiency from AI, in 2019.
Dometic group Consumer goods No AI SEK
No information of AI in annual report or online Dometic (2019). Annual reports. https://www.dometic.com/sv-se/se/om-oss/investerare/finansiella-rapporter/2019/dometic-publicerar-rsredovisni-2662469 Nothing about AI.
DRLCO (Drilling company of Oil & Gas No AI DKK 1972)/Maersk Drilling
No information of AI in annual report or online Maersk Drilling (2019). Annual Report. https://investor.maerskdrilling.com/static-files/900bde1c-2236-44bd-8896-ee7d9ad2e3b1 No info of AI.
DSV Panalpina Industrials No AI DKK
They will focus on AI in the future, is mentioned in 2019. Assume they will start in 2020 or forward. DSV (2019) Forward logistics. https://docs.dsv.com/group/corporate-marketing-and-communication/forward-logistics/#/ The future is AI.
Electrolux Consumer goods 2018 SEK
Was in production in 2018. Electrolux group (2019). AI gives the kitchen of the future a voice and a brain. https://www.electroluxgroup.com/en/ai-gives-the-kitchen-of-the-future-a-voice-and-a-brain-29502/ Has launched AI in the kitchen, but will continue. Electrolux group (2017). Electrolux Design explores the smart home of the future. https://www.electroluxgroup.com/en/electrolux-design-explores-the-smart-home-of-the-future-23769/ Will use it in the future for smart homes. SCC (2018). Artificial Intelligence & the shape of companies’ future https://www.scc.org.uk/about/news-and-insights/artificial-intelligence-the-shape-of-companies-future/ AI already in production in 2018.
Electa Health care 2017 SEK
In their 2017nreport, the company is saying they are using AI. There is no earlier news than that available. We assume they implemented it in 2017. Fierce Biotech (2018). Elekta strikes deal to build IBM watson into cancer care software https://www.fiercebiotech.com/medtech/elekta-strikes-deal-to-build-ibm-watson-into-cancer-care-software Deal with IBM in 2018, started using AI Elekte (2017). Annual Report https://www.elekta.com/investors/fileadmin/reports/annual-reports/elekta-annual-report-18-en.pdf Mentionin that they are using it.
Eliisa Oyj Telecommunications 2017 EUR
99
In the 2017 annual report Elisa said they are using AI in their processes. There is no earlier news about AI. Elisa (2019). Artificial intelligence at Elisa https://www.elisa.ee/en/ai Has been using AI for a time in some products Telecom (2018). How Elisa is Injecting Innovation with Artificial Intelligence Startups https://telecomdrive.com/how-elisa-is-injecting-innovation-with-artificial-intelligence-startups/ Using it in products Elisa (2017). Annual report https://corporate.elisa.com/attachment/content/Elisa_annual_review_2017.pdf Using it in operations
Epiroc Industrials 2018 SEK
Information about AI systems can be found first in 2018. Mobilaris (2018). Epiroc’s CEO about Mobilaris Mining Intelligence™ and understanding the customer’s mining business. https://www.mobilaris.se/newsroom/epirocs-ceo-about-mobilaris-mining-intelligence-and-understanding-the-customers-mining- business/ Using AI in 2018 as well – mobilaris. Epiroc (2019). A disruptive innovation that enables a paradigm shift in underground positioning. https://www.epiroc.com/en-af/customer-stories/2019/a-disruptive-innovation-that-enables-a-paradigm-shift-in-underground- positioning Using AI – mobilaris. Epicgrouo (2019). Annual report https://www.epirocgroup.com/content/dam/corporate/cision/reports/english/20200312_epiroc_publishes_annual_and_sustainability _report_for_2019.pdf Invested in mobilaris in 2018.
EQT Financials 2016 SEK
Started using AI 2016 Bloomberg (2019). Motherbrain Ready to take on 46 billion buyout funds. https://www.bloomberg.com/news/articles/2019-06-26/eqt-s-motherbrain-ready-to-take-on-46-billion-in-buyout-funds Used AI since 2016. EQT (2019).m Annual Report. https://www.eqtgroup.com/globalassets/shareholder-relations/ar-2019/eqt-ab-annual-report-2019.pdf Mother brain launched in 2016.
Ericsson Technology 2018 SEK
According to annual reports it started in 2018 but was ongoing 2017. No in formation of earlier usage could be found. Ericsson (2018). Annual Report. https://www.ericsson.com/48fa18/assets/local/investors/documents/financial-reports-and-filings/annual-reports/ericsson-annual- report-2018-en.pdf Where using AI. Ericsson (2017). Annual Report https://www.ericsson.com/4ac07a/assets/local/investors/documents/2017/ericsson-annual-report-2017-en.pdf Started investing in AI.
Essity Consumer goods 2018 SEK
Nothing disclosed in annual reports, but in a report from forbes. No earlier information found than 2018. Forbes (2018). How Robots Are Changing The Business Of Toilet Paper And Diapers https://www.forbes.com/sites/sap/2018/04/17/how-robots-are-changing-the-business-of-toilet-paper-and-diapers/#fe207b53939a Using AI in factories. Essity (2019). Annual report. https://www.essity.com/investors/reports/annual-reports/ Nothing in annual reports.
Evolution gaming group Consumer goods No AI SEK
No information of AI online or in the annual report Evolution gaming group (2019). Annual report https://www.evolutiongaming.com/sites/default/files/1216435.pdf No info
Fabege Financials No AI SEK
100
No information about AI online or in fabege´s annual report. Fabege (2019). Annual Report. https://www.fabege.se/siteassets/rapporter-och-kvartalspresenationer/fabege-annual-report-2018.pdf Nothing about AI.
Fast, Balder Financials No AI SEK
No information online or in annual report. Balder (2019). Annual Report. https://en.balder.se/sites/balder/files/1201615.pdf Nothing about AI.
Fenix outdoor international Consumer goods No AI SEK
Nothing online or in annual report of AI https://www.fenixoutdoor.se/wp-content/uploads/2020/04/annual_report_2019.pdf Nothing about AI.
Fiskars Oyj Consumer goods No AI EUR
No information about AI online or in annual report. Fiskars (2019). Annual Report. https://www.fiskarsgroup.com/sites/default/files/FiskarsGroup_Financial_Statements_2019.pdf Nothing of AI.
FLsmidth & Co Industrials No AI DKK
There is no information online, but says in the annual report that investments will be made -> for future. FLSmidth (2019). Annual Report. https://ml-eu.globenewswire.com/Resource/Download/fbacfbc9-bc33-4f24-a72b-7b8f221be689 Investments will be made in AI.
Fortum Utilities 2017 EUR
The Earliest information was that the company was using AI in new product 2017 Lean Heat (2017). Fortum’s new service reduces district heating costs with artificial intelligence. https://leanheat.com/2017/05/23/fortums-new-service-reduces-district-heating-costs-artificial-intelligence/ A new service uses AI. Fortum (2019). Annual report https://www.fortum.com/sites/default/files/investor-documents/fortum_sustainability_2019_2.pdf Used in 2019.
G4S PLC Industrials 2016 DKK
Based on news, they started using AI in 2016. G4S (2015). Robotics into teh future. https://www.g4s.com/news-and-insights/news/2015/01/27/robotics-into-the-future Was in a project of AI and robotics. G4S (2016). TRIAL OF NEW TECHNOLOGY TO TACKLE PRISON CONTRABAND https://www.g4s.com/en-gb/media-centre/news/2016/12/06/trial-of-new-technology-to-tackle-contraband System uses AI. G4S (2018). Annual Report https://www.g4s.com/-/media/g4s/global/files/annual-reports/integrated-report-extracts-2018/g4s-full-integrated-report- 2018.ashx?la=en&hash=EC1CA054E2048E8814C4B3D84360E4DE AI mentioned first time in 2018 report.
Genmab Healthcare 2019 DKK
Started using AI in 2019, based on news and annual report (was not mentioned in earlier reports. All information about AI in news was from 2019, same when searching for the Danish work “kunstig intelligens” Genmab (2019). Annual Report. https://ir.genmab.com/static-files/3d726b68-34c3-4af7-9135-ec060e9ab860 Uses AI in research. Genmab (2019).Genmab and Tempus Enter into Strategic Collaboration Agreement https://ir.genmab.com/news-releases/news-release-details/genmab-and-tempus-enter-strategic-collaboration-agreement/ Collaborates and start using AI.
Getinge Health care 2018 SEK
101
Started collaboration with company developing AI process in 2017, was using it in 2018 and will continue develop Getinge (2018). Getinge AI initiatives benefit customers and patients. https://news.getinge.com/getinge-ai-initiatives-benefit- customers-and-patients Is about to realise AI tool- Medical Device (2017). Getinge and Verb Surgical to develop digital surgical solution. https://www.medicaldevice-network.com/news/getinge-verb-surgical-develop-digital-surgical-solution/ Started collaboration in order to develop AI tool. Getinge (2019). Annual report. https://www.getinge.com/dam/corporate/documents/investors/annual-reports/swedish/getinge_-__rsredovisning_2019-sv- global.pdf Nothing in annual report.
GN store nord Health care 2019 DKK
Started using AI in earphones 2019. GN (2018). GN Store Nord partners with German AI technology company focused on sound analyses. https://www.gn.com/newsroom/news/2018/june/gn-store-nord-partners-with-german-ai-technology-company-focused-on-sound- analyses Partners up with german firm, to to develop AI. GN (2019). GN enhances headset and hearing solutions with state of the art Artificial Intelligence. https://www.gn.com/newsroom/news/2019/january/gn-enhances-headset-and-hearing-solutions-with-state-of-the-art-artificial- intelligence Uses AI in earphones. GN (2020). Annual Reports. https://www.gn.com/investor/financial-reports In 2018 report, saying that they when into the relationship developing AI, in 2019, launched fist earphones with AI.
Hennes & Mauritz Consumer goods 2018 SEK
Mentioning first time in 2018 annual report that the company are using AI. No earlier new than that the company are using AI 2018 could be found. More recent new talks about HM and AI https://www.forbes.com/sites/bernardmarr/2018/08/10/how-fashion-retailer-hm-is-betting-on-artificial-intelligence-and-big-data- to-regain-profitability/#3af7e71a5b00 Using AI and inveting in it, in 2018. Hm grouo (2018). Annual report. https://hmgroup.com/content/dam/hmgroup/groupsite/documents/masterlanguage/Annual%20Report/Annual%20Report%202018. pdf Using AI.
Hexagon Technology 2016 SEK
In 2016 annual report, information of aI included in business operation can be found. No earlier news of AI could be found. Hexagon (2016). Annual Report https://vp208.alertir.com/afw/files/press/hexagon/201704066485-1.pdf Mentioned first time that they are having AI in 2016 annual report. Hexagon (2015). Metrology Keynote: Automation and Industry 4.0 Summit (TV501). https://hxgnspotlight.com/hxgn-live-2015-hk-tv501/ Talking of future of automation in 2015.
Hexpol Basic Materials No AI SEK
No information online or in annual reports of AI Hexpol (2019). Annual Report http://investors.hexpol.com/afw/files/press/hexpol/202004020942-1.pdf Mentions nothing of AI in latest annual report.
Holmen Basic Materials 2019 SEK
Searching online, information about launch of AI in wood scanning was in 2019. No earlier information available, only later information. Holmen (2019). Holmen introduces digital wood scanning. https://www.holmen.com/en/newsroom/press/press-releases/2019/holmen-introduces-digital-wood-scanning/ Introduces AI based wood scanning in 2019. Holmen (2019). Annual Report https://vp165.alertir.com/afw/files/press/holmen/202003095956-1.pdf No info in annual report.
Hufvudstaden Financials No AI SEK
No information of AI online or in annual report
102
Hufvudstaden (2019). Annual Report. https://www.hufvudstaden.se/contentassets/fae06efd47714de9b379c528c2713fb0/hufvudstaden-arsredovisning-2019.pdf No info in annual report of AI.
Huhtamäki Industrials 2019 EUR
Pulpaer news (2019). Huhtamaki extends partnership with Transplace https://www.pulpapernews.com/20191210/10989/huhtamaki-extends-partnership-transplace Partnered up with other corporation in 2019, Implemented AI Huhtamaki (2018). Annual report https://www.huhtamaki.com/globalassets/global/investors/reports-and-presentations/en/2018/huhtamaki-oyj-annual-accounts-and- directors-report-2018.pdf Used AI in 2018. They use AI in 2019 to ensure sustainability work: https://ml-eu.globenewswire.com/Resource/Download/e46483ce-bc92-4eca-bcf5-1fff617d35cd
Husqvarna Consumer goods 2019 SEK
Company started with AI in end of 2018 and it was more established in 2019 Authority (2018). Alexa, start mowing! Husqvarna launches “Just ask Alexa!” for robotic mowers https://www.aithority.com/robots/alexa-start-mowing-husqvarna-launches-just-ask-alexa-for-robotic-mowers/ Connecting robots with Alexa 2018. Husqvarna (2019). Annual Report http://www.husqvarnagroup.com/sites/default/files/pr/202003118399-1.pdf Uses AI in automation of production. Husqvarna (2018). Annual Report http://www.husqvarnagroup.com/sites/default/files/pr/201903183725-1.pdf Reasearch for future, opened up AI lab. https://www.husqvarna.com/uk/lawn-garden/intelligent-and-world-leading-technology/ Uses AI in 2019.
ICA gruppen Consumer services No AI SEK
Is not using AI yet, but are about to implement it Computer Sweden (2019). AI ska göra Ica smartare – planerar sortiment och inköp. https://computersweden.idg.se/2.2683/1.722881/ai-ica-smartare-planerar-inkop Invests in AI Ica gruppen (2019). ICA Gruppen’s strategic priorities 2019. https://mb.cision.com/Main/7955/2712301/972529.pdf Invests in AI Ica gruppen (2018). Annual report https://www.icagruppen.se/globalassets/3.-investerare/5.-rapporter/arkiv---finansiellt/svenska/arkiv/2019/02.-arsredovisning- 2018/ica_gruppen_arsredovisning-2018.pdf Mentioning that AI is in the future and there is many potentials Ica gruppen (2019). Annual report https://www.icagruppen.se/globalassets/3.-investerare/5.-rapporter/arkiv---finansiellt/svenska/arkiv/2020/02.-arsredovisning- 2019/icagruppen-arsredovisning-2019.pdf Increasing research and projects of AI
Industrivärden Financials No AI SEK
There is no information online or on the web page about AI Maskin Världen (2018). Annual Report. https://www.industrivarden.se/globalassets/arsredovisningar/svenska/2018.pdf Do not mention AI/ML.
Indutrade Industrials No AI SEK
There is no information online or on the web page about AI Indutrade (2019). Annual Report https://vp299.alertir.com/afw/files/press/indutrade/202003259281-1.pdf No info of AI.
Intrum Financials 2018 SEK
103
Based on news, AI was implemented in 2018 Intrum (2018). Intrum UK launches collections chatbot. https://www.intrum.co.uk/business-solutions/newsroom/all-news/intrum-uk-launches-collections-chatbot/ Launched Chatbot in 2018. Fujitsu (2017). Europe’s Leading Credit Management Services Group Intrum Awards Global IT Outsourcing Contract to Fujitsu https://www.fujitsu.com/fts/about/resources/news/press-releases/2017/emeai-20171206-europe-s-leading-credit-management- services.html Sign contract in dec 2017 with fijutsi - provide AI among others. In the end of 2017 - returns will be in 2018. https://www.intrum.com/media/1882/ar2017_en.pdf Mentioned AI first time in 2017 annual report, but only that it opens up for opportunities, not that they have implemented it.
Investor Financials No AI SEK
No information of AI in Investor on the web page or online Investor (2019). Annual Report. https://vp053.alertir.com/afw/files/press/investor/202003263380-1.pdf No information of AI.
ISS Industrials No AI SKK
No information of AI for ISS AB online or on web page. Included AB in the search word since there is other things called ISS. Searched for Danish word “kunstig intelligence” too, but it did not produced any results of AI ISS (2019). Annual Report. http://inv.issworld.com/static-files/585d0080-ec35-497c-961f-c2d0ae3dc9f1 No information of AI.
Jeudan Financials No AI DKK
No information of AI available, not either with the danish search word “kunstig intelligens” Jeudan (2019). Annual Report. https://www.jeudan.dk/media/108677/jeudan_aarsrapport_2019.pdf No information if “kunstig intelligens”.
JM Financials No AI SEK
The company does not seem to use AI in its processes based on web-page information/ annual reports and searches online. My new desk (2020). JM to use Artificial Intelligence from Norwegian startup Spacemaker to digitalise early-stage construction http://www.mynewsdesk.com/fi/spacemaker-ai/pressreleases/jm-to-use-artificial-intelligence-from-norwegian-startup-spacemaker- to-digitalise-early-stage-construction-2974247 Only news from 2020 of AI. JM(2019). Annual report https://om.jm.se/contentassets/1af1a0864f5941fcaa867deca48d844f/wkr0006.pdf No info of AI in annual report.
Jyske bank Financials - Banks 2018 DKK
Magasin (2018). JYSKE BANK TAGER HUL PÅ BRUG AF AI http://magasin.samdata.dk/jyske-bank-tager-hul-paa-brug-af-ai/ Implement AI in 2018 Cognitive business (2018). UK Fintech Munnypot Partners with Jyske Bank on Robo-Advisor. http://cognitivebusiness.news/uk-fintech-munnypot-partners-jyske-bank-robo-advisor/ Partners up with fintech startup Jyske bank (2019). Annual Report. https://investor.jyskebank.com/wps/wcm/connect/jbc/7737b675-fbe8-463a-a9f7- a0bb3d41bf37/Jyske+Bank_Annual+Report+2019.pdf?MOD=AJPERES&CONVERT_TO=url&CACHEID=ROOTWORKSPAC E.Z18_P20418S0N05640Q0MBPDFT1666-7737b675-fbe8-463a-a9f7-a0bb3d41bf37-n2ANQra No mentioning of AI in annual report
Kemira Basic materials 2019 EUR
104
Started developing AI in 2017 and are using it in 2019. No information from 2018 mentioned, therefore it can be assumed that it was used first in 2019. The search word Tekoäly (finnish AI) was used as well Kemira (2017). Big Data applications and advances for the water treatment industry https://www.kemira.com/company/media/newsroom/news/big-data-applications-and-advances-for-the-water-treatment-industry/ Developing AI. Kemira (2019). Predicting the future at hygienic board mills. https://www.kemira.com/insights/predicting-the-future-at-hygienic-board-mills/ Using AI in 2019. Kemria (2019). Annual Report. https://media.kemira.com/kemiradata/2020/02/kemira-report-2019.pdf Nothing about AI mentioned in annual reports.
Kesko Oyj Consumer services 2017 EUR
According to their annual report 2017 the company where using AI. In 2016 nothing of AI was mentioned. No earlier news about the company's usage of AI could be found Kesko (2018). K GROUP ANNOUNCES ETHICAL PRINCIPLES FOR UTILISING AI: THE BEST INTERESTS AND NEEDS OF OUR CUSTOMERS COME FIRST https://www.kesko.fi/en/media/news-and-releases/news/2018/k-ryhma-julkistaa-tekoalyn-hyodyntamisen-eettiset-periaatteet- asiakkaan-etu-ja-tarve-etusijalla/ Using aI in 2018 Kesko (2017). Annual Report. https://www.kesko.fi/globalassets/03-sijoittaja/raporttikeskus/2018/kesko_annual_report_2017.pdf “Utilises AI in products”.
Kindred group Consumer service No AI SEK
The company are doing research projects of AI but it seems like they have not implemented any AI systems. Mentioning in annual report that it might be possible in 2023 Kindred group (2018). Using Artificial Intelligence in the fight against Money Laundering https://www.kindredgroup.com/news--insights/2018/using-artificial-intelligence-in-the-fight-against-anti-money-laundering/ 2018 partners in order to research AI and how it can be used working against money laundry. https://www.kindredgroup.com/globalassets/documents/investor-relations-related-documents/financial-reports/kindred-annual- report-2019-eng.pdf Mentioning in latest annual report that by 2023 AI most probably be used to foresee gambling problems etc. Not Today.
Kinnevik Financials 2019 SEK
According to the information online, 2019 was the year kinnevik started using AI, after investing in a project 2016. Kinnevik (2019). Kinnevik participates with USD 50 million in Babylon’s funding round. https://www.kinnevik.com/media--contact/press-releases/2019/8/2146788-Kinnevik-participates-with-USD-50-million-in- Babylons-funding-round Invested in company for creating AI in 2016, which succeeds and in 2019 they use it in their operations. Kinnevik (2019). Annual Report https://www.kinnevik.com/globalassets/documents/2.-investors/reports/2019/annual-report/ar_2019_e.pdf No disclosure of AI.
Klövern Financials No AI SEK
There is no information about Klövern using AI on the web page of the company or online. Klövern (2019). Annual Report https://www.klovern.se/globalassets/dokument/irpress/finansiella-dokument/2019/eng/kl_ar19_eng.pdf Nothing about AI.
Kojamo Oyj Financials - Real estate 2017 EUR
Started with AI in 2017 based on news and annual reports. Kajamo (2017). Annual report. https://vuosikertomus2017.kojamo.fi/pdf/Kojamo_Annual_Report_2017.pdf AI is used in heating systems. Kojamo (2017). Artificial intelligence ensures that Lumo apartments are comfortable, regardless of the weather. https://kojamo.fi/en/news-releases/artificial-intelligence-ensures-that-lumo-apartments-are-comfortable-regardless-of-the-weather/ Using AI in 2017 in heating systems.
Kone oyj Industrials 2017 EUR
105
Based on research online and Kone´s webpage, the company implemented AI in 2017. Kone (2018). Tools of tomorrow. https://www.kone.com/en/news-and-insights/stories/tools-of-tomorrow.aspx Uses AI in 2018. Kone (2017). KONE wins order for Neva Towers in Moscow, Russia https://www.kone.com/en/news-and-insights/releases/kone-wins-order-for-neva-towers-in-moscow--russia-2017-04-26-3.aspx Uses AI in 2017 IBM. A billion people a day. Millions of elevators. No room for downtime. https://www.ibm.com/watson/stories/kone/ Project with IBM to develop AI. Salesforce (2019). Hissen går hela vägen upp hos Kone – tack vare artificiell intelligens. https://www.salesforce.com/se/blog/2019/12/kone-artificial-intelligence.html Uses AI in elevators.
Konecranes Oyj Industrials 2017 EUR
Implemented aI in 2017 according to news. Did not disclose much about this in annual report more than mentioning machine learning was researched. Symbio (2017). Konecranes and Symbio use artificial intelligence and robotics to develop an autonomous warehouse https://www.symbio.com/konecranes-symbio-use-artificial-intelligence-robotics-develop-autonomous-warehouse/ Uses AI in 2017. Konecrane (2017). Annual report https://www.konecranes.com/sites/default/files/investor/konecranes_annual_report_2017_1.pdf In the annual report, they are only mention that they are researching on machine learning.
Kungsleden Financials - real estate No AI SEK
No information of AI online or on webpage. Kungsleden (2019). Annual report https://www.kungsleden.se/contentassets/97b16a43e2c4499ea74c36015a1b106a/kungsledens-arsredovisning-for-2019 Nothing about AI.
KBHL, Kobenhavns lufthavne Industrials 2018 DKK
Used at 2018 according of search online. Nothing earlier found. Madsen, K (2018). Brug af kunstig intelligens au københavns lufthavne. http://mobil.dau.dk/Content/file_knowledge_item/DAu_AI_Koebenhavns_Lufhavne_310_INT.pdf Uses AI.
Latour Financial No AI SEK
There is no information about AI online or on webpage. Latour (2019). Annual report https://latour.se/assets/latour-ar-2019-se---web-singlepages-1583933810.pdf Nothing about AI
Lifco Industrials No AI SEK
There is no information about AI online or on webpage. Lifco (2019). Annual report https://mb.cision.com/Main/5431/3068151/1217533.pdf Nothing about AI.
Loomis Industrials No AI SEK
There is no information about AI online or on webpage. Loomis (2019). Annual Report. https://vp274.alertir.com/afw/files/press/loomis/202004032508-1.pdf Nothing about AI.
Lundbeck85 Health care - healthcare 2017 DKK
Started using AI in 2017. IBM (2017) IBM Watson Health and Lundbeck collaboration on use of artificial intelligence https://www.ibm.com/blogs/nordic-msp/ibm-watson-health-and-lundbeck-collaboration-on-use-of-artificial-intelligence/ Started using AI 2017. Lundbeck (2019). Lundbeck commences usage of artificial intelligence in drug discovery https://www.lundbeck.com/global/about-us/features/2019/lundbeck-commences-usage-of-artificial-intelligence-in-drug-discovery Also using AI in 2019.
106
Lundbergsföretagen Financials No AI SEK
No information of AI. Lundbergsföretagen (2019). Annual report https://www.lundbergforetagen.se/sites/default/files/files/Lundbergs_AR2019_web.pdf Nothing mentioned
Lundin Energy Oil and Gas No AI SEK
No information of AI. Lundin Energy (2019). Annual report https://www.lundin-energy.com/sv/media/finansiella-rapporter/ No AI mentioned.
Lundin mining corporateon Basic materials No AI SEK
Lundin Mining Corporation (2019). Annual reporthttps://www.lundinmining.com/site/assets/files/8020/lundin_mining_- _2019_ye.pdf No AI mentioned
Marel Industrials 2018 ISK
Where using AI in 2018 Marel (2017). How technology and innovations are impacting the seafood industry. http://ar2017.marel.com/delivering-growth/innovation In 2017, mentions that AI and ML will be used in the future. Marel (2018). How technology and innovations are impacting the seafood industry. https://eu.eventscloud.com/file_uploads/da52b6b20796053f77c6853c85cec580_Marel-SigurdurOlason.pdf Used AI in 2018.
Medicover Health care 2019 SEK
Medicover (2020) https://www.medicoverhospitals.in/robotic-surgery/ Using AI, robot surgery Projectsmonitor (2020). Europe’s Medicover Global to expand operations in Telugu States https://www.projectsmonitor.com/daily-wire/europes-medicover-global-expand-operations-telugu-states/ Using AI in 2020 Medicover (2017). E-health is much more than online prescription http://biuroprasowe.medicover.pl/24771-e-health-is-much-more-than-online-prescriptions Testing AI in 2017 Medicare (2019). Annual Report https://vp260.alertir.com/afw/files/press/medicover/202003318576-1.pdf Using AI in 2019, using it for developing integrated diagnostics.
Metso oyj Industrials 2019 EUR
Metso (2018.11). Digitalization as a matter of sustainability. https://www.metso.com/blog/metso-world/digitalization-as-a-matter-of-sustainability/ In the end of 2018, talking about how AI will be used it the future of metso. Metso (2019). RPA and AI – enabling smarter ways of working https://www.metso.com/blog/metso-world/smarter-working-with-rpa-and-ai/ Chatbot will be first step towards AI. Metso (2019.05.31). Facebook. https://m.facebook.com/MetsoWorld/photos/a.439547729420164/2792223280819252/?type=3&source=54 Introduces chatbot in 2019.
Metsä board oyj Basic materials 2019 EUR
Implemented AI in 2019, available on their own web page and in many news. No news about AI in Metsä board before that, no information in 2018 annual report. Metsä board (2019). Metsä Board implements artificial intelligence in quality management https://www.metsaboard.com/Media/Product-news/Pages/metsa-board-implements-artificial-intelligence-in-quality- management.aspx Implemented AI in 2019 Metsä board (2019). Annual report. https://www.metsaboard.com/MaterialArchive/Annual-reports-and-summaries/Metsa-Board-Annual-report-2019.pdf Also mentioned in annual report.
TIGO SDB, Millicom int. Telecommunications No AI SEK cellular SDB
107
No AI Millicom (2019). Annual report https://www.millicom.com/AnnualReport2019Millicom/PDF/2019-annual-report.pdf Nothing about AI
Modern times group Consumer services No AI SEK
No AI in the news or in the webpage Mtg (2018). Annual Report https://www.mtg.com/wp-content/uploads/2019/04/MTG-Årsredovisning-2018.pdf Latest published report, nothing about AI.
Mycroinic Industrials No AI SEK
Have not yet implemented AI, but are about Mycronic (2019). Beating the best with artificial intelligence https://www.mycronic.com/sv/news/news-articles/beating-the-best-with-articial-intelligence/ Started researching AI in 2019 Mycronic (2019). Annual report https://www.mycronic.com/globalassets/annual/pdf/ars--och-hallbarhetsredovisning-2019.pdf A step towards implementing AI, researching it now.
NCC Industrials No AI SEK
NCC has statred investing in researching AI, but is not there yet NCC (2019). NCC använder AI för säkrare lyft https://www.ncc.se/media/pressrelease/dd7de280a6e53d10/ Testing AI NCC group (2019). Project Ava: On the matter of using Machine Learning for web application security testing – Part 1: Understanding the basics and what platforms and frameworks are available https://www.nccgroup.trust/uk/about-us/newsroom-and-events/blogs/2019/june/project-ava-on-the-matter-of-using-machine- learning-for-web-application-security-testing-part-1-understanding-the-basics-and-what-platforms-and-frameworks-are-available/ Testing AI NCC (2019). Annual Report https://www.ncc.se/siteassets/investor-relations/arsredovisning/ncc_ar_swe_190315.pdf Latest report is 2018, mentions nothing of AI
Neste oyj Oil and gas No AI EUR
Has started research and development in AI but not yet implemented it Neste (2019). Annual Report https://www.neste.com/corporate-info/news-inspiration/material-uploads/annual-reports Only mentions AI in terms of external cyber risks. Neste (2019) Neste solutions and curious AI to develop advanced AI technology for process industry. https://www.neste.com/releases-and-news/research-and-development/neste-engineering-solutions-and-curious-ai-develop- advanced-ai-technology-process-industry R&D of AI
Netcompany group Technology 2019 DKK
Netcompany (2019). COgnitive computing. https://www.netcompany.com/int/Service-Lines/Cognitive-Computing Are working on AI. Netcompany (2020). Less unexpected waiting time is a way to improve the customer experience. At the same time utilisation of the race tracks for baggage is optimised. https://www.netcompany.com/int/Case-studies/CPH-Airports Using AI, but there is no date. Netcompany (2018). Annual report https://www.netcompany.com/int/News/Annual-report-2018 mentions that they will follow the AI development. Netcompany (2019). Annual report https://www.netcompany.com/int/News/Annual-report-2019 Mentions that old systems are being replaced with AI etc.
Nibe industrier Industrials No AI SEK
108
No information of AI online or on webpage/annual report NIBE (2019). Q3 report https://issuu.com/nibegroup/docs/q3_se_2019-w?fr=sNjVmYTE3OTg3Mg Noting of AI. NIBE (2018). Annual report. https://issuu.com/nibegroup/docs/nibe_se_ar_2018?e=30789445/69155450 Nothing of AI, latest published report.
Nobia Consumer goods No AI SEK
No information of AI online or on webpage/annual report Nobia (2019). Annual Report http://www.nobia.com/globalassets/documents/reports/2019/nobia-arsredovisning-2019.pdf No info of aI
Nokia Technology 2017 EUR
According to annual reports, Nokia started use AI in 2017. Nothing was mentioned in 2016, not in nes or in annual report. https://www.nokia.com/networks/services/analytics-and-ai-services/ Using AI https://www.nokia.com/blog/four-most-promising-applications-artificial-intelligence-telecom/ In 2018, talking about producing AI Nokia (2017). Annual report https://www.nokia.com/system/files/files/nokia_20f17_full_web_1.pdf Used AI in 2017.
Nokian renkaat, TYRES Consumer goods No AI EUR
Introduces, but nothing mentioned that it is yet used Nokian tyres (2019). FUTURE TIRES WILL BE SMART – DRIVERS WANT TIRES THAT CAN REACT TO WEATHER https://www.nokiantyres.com/company/news-article/future-tires-will-be-smart-drivers-want-tires-that-can-react-to-weather/ AI is being introduces Nokia Tyres (2019). reports archive https://www.nokiantyres.se/foretaget/pressmeddelanden-och-publikationer/arsredovisnings/ Nothing in annual reports
Nolato Industrials - Industrial No AI SEK goods and services
No info online or on webpage/annual report Nolatos (2019). Annual report https://cms-pdf.nolato.com/simple_document.aspx?doc=7C9FD5ADC953499B9EEF3A9676BF5562.pdf No info of AI
Nordea bank FI Financials 2017 EUR
Started using AI in 2017. Nothing mentioned in 2016 Nordea (2017). Speeding up our customer response time with AI https://www.nordea.com/en/press-and-news/news-and-press-releases/news-group/2017/ai-partnership-nordea.html Uses AI in 2017, for customer service Nordea (2017). Annual Report https://www.nordea.com/Images/37-247332/Årsredovisning%20Nordea%20Bank%20AB%202017.pdf Introduced chatbot in 2017
Nordic entertainment group Consumer services 2019 SEK
Saying in annual report 2019 that they are using AI, which is not mentioned in 2018 Nentgrouo (2019). annual and sustainability report. https://www.nentgroup.com/sites/default/files/documents/AGM%202020/nent_group_annual_and_sustainability_report_2019.pdf “have developed algorithms, used of machine learning and artificial intelligence
Novo Nordisk Health care 2019 DKK
109
In 2019 annual report, the company are stating that they are using AI. Npt mentioned earlier. News are from 2016 providing information that NN are investing in research of AI Novo nordisk (2020). modeling and data scientist. https://www.novonordisk.com/careers/professionals/research-and-development/who-are-we-looking-for/modelling-data- scientists.html Has applied AI and started reseaching in 2016. Pharma world magazine (2018). New ai based rd model for novo nordisk. https://www.pharmaworldmagazine.com/a-new-ai-based-rd-model-for-novo-nordisk/ Investing heavily in R&D AI in 2018 Novo Nordisk (2019). Annual report https://www.novonordisk.com/content/dam/Denmark/HQ/investors/irmaterial/annual_report/2020/Novo-Nordisk-Annual-Report- 2019.pdf Using AI
Novozymes Health care No AI DKK
The company are investing in AI R&D but there is no information that they have implemented it. Novozymes (2010). Robots do science https://www.novozymes.com/en/news/news-archive/2010/03/45728 Researching in 2010. Ryffin (2019). Riffyn Enters Multi-Year Contract with Novozymes for R&D Data Analytics https://riffyn.com/press-releases/2019/6/5/riffyn-enters-multi-year-contract-with-novozymes-for-rampd-data-analytics Still researching.
Nyfosa Financials 2019 SEK
Nothing in the news, but in the latest annual report the company mention that they are using aI Nyfosa (2019). Annual report https://mb.cision.com/Main/17648/3089735/1230537.pdf Using AI in 2019
Orion oyj Health care No AI EUR
There is not much news about this company and usage of AI. Neither information on their webpage. Orion (2019) Artificial intelligence provides inspiration in drug development. Meantiones that the company has been using AI for several years, but other articles are talking about that they are investing in AI research. https://www.orion.fi/en/rd/research-for-patient-well-being/artificial-intelligence-provides-inspiration-in-drug-development/ Are using AI in research and development in 2019, and have been using it for several years
Outokumpu Basic resources No AI EUR
Not much news about AI, mentioned in 2019 that they will start. So in the near future the company will use AI Outokumpu (2019). Outokumpo moves forward with digital manufacturing https://www.outokumpu.com/sv-se/news/2019/outokumpu-moves-forward-with-digital-manufacturing. Mentioning that they will use AI https://www.outokumpu.com/investors/materials Nothing about AI in report.
Pandora Telecommunication 2018 DKK
According to new, pandora are using AI in an app and in facilities that was inaugurated 2018. Nothing is mentioned in thero annual reports Phymsts (2019). Pandora debuts voice assistant https://www.pymnts.com/news/artificial-intelligence/2019/pandora-music-voice-assistant-commands/ Uses AI in app 2019 Pandora (2019). Annual reprot. https://pandoragroup.com/staticcontent?url=http://investor.pandoragroup.com/static-files/84d758a8-9d17-4e4b-bfa6- 59e4dd29ec51&format=pdf&title=Annual%20Report%202019 Nothing in annual report. GlobesNewswire (2018). PANDORA INAUGURATES NEW LEED GOLD CERTIFIED CRAFTING FACILITY. https://www.globenewswire.com/news-release/2018/06/15/1525044/0/en/PANDORA-INAUGURATES-NEW-LEED-GOLD- CERTIFIED-CRAFTING-FACILITY.html Uses AI in 2018, inaugurated in 2018.
Pandox Financials 2019 SEK
110
Nothing about AI in the news. In the latest annual report, the company mentioned that they have started using a new system which is using AI. Pandox (2019). Annual report https://www.pandox.se/contentassets/c95b20046f0f42f0afbf292d61634b55/pan_ar19_en_indexerad.pdf Use AI in 2019
Peab Industrials No AI SEK
Byggindustrin (2019). Så jobbar bygg med AI idag https://byggindustrin.se/artikel/nyhet/sa-jobbar-bygg-med-ai-i-dag-28352 Are not using AI by august 2019 Klimator (2019. Klimator and artificial intelligence transforms and digitalises Peab's winter road maintenance. https://www.klimator.se/news/klimator-and-artificial-intelligence-transforms-and-digitalises-peabs-winter-road-maintenance In october 2019 undergoing a change, about to implement AI Peab (2019). Annual report. https://peab.inpublix.com/2019/?s=artif Noting in annual report.
Ratos Financials No AI SEK
Nothing in the news or in the company´s webpage about AI Ratos (2019). Annual report https://www.ratos.se/globalassets/global/05_investor-relations/delarsrapporter/rapportarkiv/rapportarkiv_eng/2019_ar.pdf Nothing about AI
Resurs holdings Financials 2018 SEK
Company introduces AI in 2018, disclosed on their webpage Resurs holding (2018). Resurs Holding Year-end Report January—December 2018 https://www.resursholding.se/en/resurs-holding-interim-report-january-september-2018/ They introduced it in 2018 Resurs holding (2019)- Resurs Holding Year-end Report January—December 2019 https://www.resursholding.se/sv/resurs-holding-delarsrapport-januari-september-2018/ Use AI in 2019
Ringkjöbing landbobank Financials No AI DKK
There is no news online or any information about AI on the web page- NEither when searching for kunstig intelligens Ringkjöbing landbobank (2019). Annual Report https://ml-eu.globenewswire.com/Resource/Download/44af4da2-8b55-46c5-b2a5-12130654ca57 Nothing in annual report about AI
Rockwool int Industrials 2019 DKK
The company mentions that they implemented AI in 2019. Informatica (2019). Future-Proofing Digital Transformation. https://www.informatica.com/content/dam/informatica-com/en/collateral/customer-success-story/rockwool-group_customer- profile_3751.pdf Uses AI in 2019 Industry (2017). AI&EDGE. How to Bring Value? https://industri40.ida.dk/wp-content/uploads/2020/02/Alex-Severin-Rockwool.pdf Talking about AI in future in 2017 Rockwool (2019). Annual report https://ml-eu.globenewswire.com/Resource/Download/9a4fe39b-4379-41e0-983d-3e7e17f825b8 Do not meantion AI
Royal unibrew Food and beverage No AI DKK
There is no information in the news or in the company web page about AI Royal unibrew (2019). Annual report http://investor.royalunibrew.com/node/20356/pdf Do not mentioning AI
SAAB Industrials 2019 SEK
111
Nyteknik (2018). Saab. Saab Surveillance: Vi är på väg att bli ett mjukvaruföretag. https://www.nyteknik.se/premium/saab-surveillance-vi-ar-pa-vag-att-bli-ett-mjukvaruforetag-6933523 Company are about to adopt to new data/AI Saab (2019). Artificial Intelligence: A powerful tool to develop sensors https://saabgroup.com/investor-relations/short/interim-report-q1-2019/a-powerful-tool--to-develop-sensors/ Are about to take on all the opportunities of AI Saab (2019). Annual report https://saabgroup.com/globalassets/corporate/investor-relations/reports/2019/annual/saab_asr_2019.pdf Investing in AI Saab (2018). Annual report https://saabgroup.com/globalassets/corporate/investor-relations/presentations/saab_annual_report_2018.pdf Tap into the opportunities of AI Saab (2019). A revolutionary technology https://saabgroup.com/media/stories/stories-listing/2019-06/a-revolutionary-technology/ Are using AI in 2019
Sagax Financials No AI SEK
No information online, on the web page or in latest annual report. Sagax (2018). Annual reprort https://vp261.alertir.com/afw/files/press/sagax/201904126640-1.pdf Nothing in annual report
SBB - Samhällsbyggnadsbo i Financials No AI SEK norder
No information online, on the web page or in latest annual report. SBB (2019). Annual report https://sbbnorden.se/wp-content/uploads/2020/03/Årsredovisning-SBB-2019_v200325-FINAL-1.pdf Nothing about AI
Sampo oyj Financials No AI EUR
Smapo are talking about that they will have to develop and follow AI trend, but not that they have implemented AI Sampo (2020). Latest outlook. https://www.sampo.com/investors/sampo-as-an-investment/latest-outlook/ Outlook of AI 2020. Sampo (2020). Financial reports https://www.sampo.com/year2019/?utm_source=Sampo.com&utm_medium=referral#group-reports The company are talking about that AI is a force and that they have to develop but does not mention that they have implemented it.
Sandvik Industrials 2017 SEK
Sandvik has used AI from 2017 according to its annual report. Sandvik (2019) AI supports agile working methods https://www.home.sandvik/en/stories/articles/2019/09/ai-supports-agile-working-methods/ using AI 2019. Sandvik (2018). Annual report https://www.home.sandvik/globalassets/4.-investors/reports-presentations/annual-reports/annual-report-2018.pdf Has successfully adopted AI. Sandvik (2017). Annual report https://www.home.sandvik/globalassets/4.-investors/reports-presentations/annual-reports/annual-report-2017.pdf Has adopted, using automated mines etc.
Sanoma Oyj Consumer services No AI EUR
No new or information of usage of aI. Neither when searching for tekoäly Sanoma (2020). Report. shttps://sanoma.com/investors/reports-and-presentations/ No info of aI Sanoma (2015). Sanoma Ventures invests in language learning technology provider ITSLanguage https://sanoma.com/release/sanomaventures-invests-in-language-learning-technology-provider-itslanguage/ invested in tech company using IA
SCA Basic Materials No AI SEK
112
The first project in the forest industry for applying AI is being done in SCA Obbola in 2020. https://www.processnet.se/article/view/697419/sca_effektiviserar_genom_ai This technique was under testing in 2019. https://www.sca.com/sv/nyheter/2019-12/flera-steg-framat-for-deltagarna-i-forest-business-accelerator-2019/ https://mb.cision.com/Main/600/3053862/1206818.pdf Nothing in annual report
Scandinavian Tobacco Group Consumer Goods No AI DKK
Nothing about AI online, nor in the annual report of 2019: https://www.st-group.com/2020-02-26_Annual_Report_2019.pdf
Schouw & Co. Industrials No AI DKK
Nothing in the annual report about AI: https://www.schouw.dk/media/1788/schouw-annual-report-2019-eng.pdf First place in microsoft dynamics 365 & AI contest in 2019 with a concept. But no information online that they use AI in their business.
SEB A & C Financials 2016-2019 SEK
Through its use of an AI agent called Aida as a chat bot and for internal it support, SEB has gained an award in 2017: https://sebgroup.com/press/news/seb-awarded-for-innovative-use-of-ai-technologyFurthermore, Aida was first set up in early 2017: https://www.voister.se/artikel/2017/12/ett-ar-med-sebs-chattbot-aida/ In 2016, they had a customer support robot called Amelia: https://digital.di.se/artikel/seb-tar-in-robot-pa-kundtjansten No mention of AI usage online or in annual reports earlier than 2016.
Securitas B Industrial 2018-2019 SEK
In the 2019 annual report, Securitas mention that they use machine learning to understand security incident risks: https://www.securitas.com/globalassets/com/files/annual-report-pdf/securitas_annual_and_sustainability_report_2019.pdf. Also utilizing machine learning for security incident response in 2018: Securitas (2018, p. 13) https://www.securitas.com/globalassets/com/files/annual-reports/en/securitas_annual_and_sustainability_report_2018.pdf In 2017, it was planned to in the future utilize AI for a competitive edge: https://www.securitas.com/globalassets/com/files/annual- report-pdf/securitas_annual_report_2017.pdf
SimCorp Technology No AI DKK
AI in the works but not launched in 2019: https://www.simcorp.com/en/insights/journal/2019/machine-learning-supporting-the- full-value-lifecycle
Skanska B Industrials No AI SEK
In 2019 they were only exploring how AI could be implemented, and it was under research and development: https://group.skanska.com/496a54/siteassets/investors/reports-publications/annual-reports/2019/annual-and-sustainability-report- 2019.pdf They are in a reesarch project about AI as of https://group.skanska.com/sv/media/241844/Skanska-deltar-i-forskningsprojekt-for- att-begransa-koldioxidutslapp-med-artificiell-intelligens
SKF A & B Industrials No AI SEK
SKF aquires industrial AI company in late 2019 and the aquisition was expected to be complete in last quarter 2019. https://www.skf.com/africa/en/news-and-media/news-search/2019-Oct-07-SKF-acquires-industrial-AI-company- 3438316.htmlThey are welcoming the new team the 26th of december 2019. http://evolution.skf.com/skf-acquires-industrial-ai- company/ Not really taking advantage of AI until 2020.
Spar Nord Bank Financials No AI DKK
As of 2017 they were only internally testing AI technology: https://www.finansforbundet.dk/da/nyheder- aktuelt/Sider/Sparnordtesteraiiegeninkubator.aspx Nothing about AI in 2019 annual report
SSAB A & B Basic Materials 2018-2019 SEK
SSAB has since 2018 used AI in order to improve safety at work: https://www.ssab.com/news/2018/03/ssab-is-improving-safety- at-work-with-the-help-of-ai No information from before 2018 online or in annual reports.
Stora Enso Oyj A, Oyj R & R Basic Materials 2018-2019 SEK
113
Started investing in their digitalization programme in 2016, and as of 2019 has completed projects including AI. https://www.manufacturingglobal.com/company/stora-enso-future-manufacturing-just-got-smarter# Uses AI in 2018, for example finance delivery robots including one AI solution: https://www.storaenso.com/- /media/documents/download-center/documents/annual-reports/2018/storaenso_annual_report_2018.pdf However, no mention of AI in 2017: https://www.storaenso.com/-/media/Documents/Download-center/Documents/Annual- reports/2017/STORAENSO_Financials_2017.ashx
Sv. Handelsbanken A & B Financials 2018 SEK
Harnessing AI in 2018 in order to review financial advising and to enhance the mortgage loan process https://vp292.alertir.com/afw/files/press/handelsbanken/201902138331-1.pdf Testing and experimenting with AI in 2017: https://computersweden.idg.se/2.2683/1.682161/handelsbanken-fintech Also in 2017, they see opportunities with AI but no information that they use it in their business: https://vp292.alertir.com/afw/files/press/handelsbanken/201802158815-1.pdf AI plans for 2020: https://vp292.alertir.com/afw/files/press/handelsbanken/202002135859-1.pdf
SWECO A & B Industrials No AI SEK
No info regarding AI in the annual report: https://www.sweco.se/globalassets/ir/2020/agm- eng/sweco_ar19_final_eng_links.pdf.pdf, nor online
Swedbank A Financials 2016 SEK
In 2016: Swedbank gone live with a virtual assistant for a human like customer experience: https://www.fintechfutures.com/2016/04/swedbank-sweet-on-virtual-nina/ Also: Swedbank still uses AI in 2019 to enhance the customer experience: https://www.di.se/brandstudio/swedbank/swedbank- anvander-ai-for-att-forbattra-kundupplevelsen/
Swedish Match Consumer Goods No AI SEK
No info regarding AI in the annual report: https://www.swedishmatch.com/globalassets/reports/annual- reports/2019_swedishmatchannualreport_interactive_en.pdf nor online.
Swedish Oprhan Biovitrum Healthcare No AI SEK
No info regarding AI in the annual report: https://www.sobi.com/sites/default/files/pr/202004074526-1.pdf nor online.
Sydbank Financials No AI DKK
No AI usage mentioned in annual report or online: https://ipaper.ipapercms.dk/Sydbank/aarsrapport-2019/sydbank-aarsrapport- 2019-ukpdf/#/
Tele2 A & B Telecommunications No AI SEK
Working on AI projects in 2019. but they are not launched. They also don’t seem to have any chat bothttps://www.tele2.com/media/press-releases/2019/reduced-energy-consumption-in-tele2s-network-through-international- research-project https://www.tele2.se/foretag/nyheter/hallbarhet-ai
Telia Company Telecommunications 2018 SEK
2018: Telia is launching ACE http://press.telia.se/pressreleases/telia-lanserar-ace-en-ny-plattform-foer-framtidens-kundmoete- 2559439 More AI focus in 2019: https://www.teliacompany.com/globalassets/telia-company/documents/reports/2019/telia-company-- annual-and-sustainability-report-2019.pdf & https://www.teliacompany.com/globalassets/telia- company/documents/reports/2018/annual-report/telia-company--annual-and-sustainability-report-2018.pdf
Terveystalo Oyj Healthcare 2018 EUR in 2018, they aquired shares in Etsimo healthcare to harness AI in order to assist customers and healthcare professionals: https://www.avanza.se/placera/pressmeddelanden/2018/12/03/terveystalo-oyj-press-release-terveystalo-to-become-a-shareholder- in-startup-company-etsimo-healthcare-harnessing-ai-to-assist-the-customer-and-the-healthcare-professional.html
Thule Group Consumer Goods No AI SEK
Tieto EVRY oyj Technology 2016 EUR
114
First nordic company to apply AI to their leadership team in 2016: https://www.tieto.com/en/newsroom/all-news-and- releases/corporate-news/2016/10/tieto-the-first-nordic-company-to-appoint-artificial-intelligence-to-the-leadership-team-of-the- new-data-driven-business/
Topdanmark Financials 2018 DKK
Insurance and pension company. Launched chatbot in late 2018: https://topdanmark.com/en/innovation/chatbot/
TRATON Industrials 2018 SEK
A subsidiary of Volkswagen AG. A commercial vehicle manufacturer. Man and Scania for example is developing AI features for autonomous driving in 2018 and 2019. They are being used as tippers in mines and on highways. https://traton.com/en/innovation/automated-driving.html
Trelleborg B Industrials No AI SEK
No information found about them using AI. However, they produce sealing materials with applications for e.g. AI: https://www.industritorget.se/nyheter/trelleborg+lanserar+unikt+t%C3%A4tningsmaterial+f%C3%B6r+dynamiska+applikationer/ 26112/
Tryg Financials 2019 DKK
In 2019, Tryg uses conversational AI for their insurance. Chatbots? https://www.boost.ai/articles/danish-insurance-company-tryg- resolves-97-of-all-internal-chat-queries-using-conversational-ai 60% of internal chat queres uses AI. The chatbot is called Rosa. Also they have an internal chatbot called Mia. They are also looking to further include AI in their insurance claims in the future. https://mobilemarketingmagazine.com/tryg-conversational-ai-chatbots They began working on Rosa in summer 2018, but it seems to have been launched in 2019: https://www.uctoday.com/contact- centre/tryg-launch-new-ai-powered-virtual-agent/ No full financial statements from 2019 though.
UPM-Kymmene Oyj Basic Materials No AI EUR
Providing alternatives for fossil based materials. For example special “plastics” from the forest. Sustainability emphasizing. The upright project measured the net impact of the company’s operation on society throug AI, but I cannot find any information that UPM uses AI in their business? https://www.upm.com/news-and-stories/articles/2019/05/artificial-intelligence-measures- business-impact/
Wallenstam B Financials No AI SEK
Hans Wallenstam is critical of swallowing all new technology. http://www.trendmiljo.se/hans-wallenstam-svalj-inte-all-ny-teknik/
Valmet Oyj Industrials 2018 EUR
A leading company in supplying tech, automation (automation is not necessarily equal to AI) and services for certain industries involving energy, pulp and paper Founded in 2013. They use AI in 2019, incorporated in production planning and control. https://www.valmet.com/media/articles/experts- voice/predicting-the-future-using-data/ first mention of AI: 2018, Valmet Industrial Helmet that utilizes AI: https://www.valmet.com/globalassets/media/events/2018/customer-days-2018/opening- presentation/vcd2018_opening_jari_almi.pdf?_t_id=1B2M2Y8AsgTpgAmY7PhCfg%3d%3d&_t_q=artificial+intelligence+AI&_t _tags=language%3aen%2csiteid%3a5e972700-3605-4cb7-bfa8- 0a11af9fa784&_t_ip=85.224.73.9&_t_hit.id=Knowit_ValmetCom_Models_Media_GenericMedia/_01e3e459-9b8e-4168-b1fb- d9b796ff063f&_t_hit.pos=23
VEONEER SDB Consumer Goods 2018 SEK
Provides automated driving to vehicle manufacturers globally. In Sweden 2019, they partner in AI innovation: https://news.cision.com/veoneer/r/veoneer-partner-in-ai-innovation-of-sweden,c2733817 Founded in 2018, https://www.veoneer.com/sites/default/files/2018-Veoneer-Inc-Annual-Report.pdf they seems to always have been involved in AI, its kind of their thing.
Vestas Wind Systems Oil & Gass 2012 DKK
Vestas aquires AI in order to boost wind power in 2018: https://news.bloombergenvironment.com/environment-and-energy/vestas- buys-artificial-intelligence-to-boost-wind-power since 2017, contract with outsourced machine learning for management https://diginomica.com/how-vestas-wind-systems-used-outsourced-machine-learning-to-transform-contract-management uses AI in 2012 for trouble shooting. not best source https://www.tv2nord.dk/nordjylland/nordjysk-software-i-vindmoller But also they are listed as a customer: https://www.dezide.com/customers/vestas-captures-global-knowledge-experience-dezide/
115
WIHLBORGS Financials No AI SEK FASTIGHETER
No Information of AIhttps://www.wihlborgs.se
Vitrolife Healthcare 2019 SEK
Aquires AI tech in 2019 https://www.vitrolife.com/investors/press-releases/2019/vitrolife-acquires-artificial-intelligence- technology-for-embryo-assessment/ nothing before 2019
Volvo A & B Industrials 2019 SEK
Volvo partners with NVIDIA to develop AI for self driving trucks: https://www.volvogroup.com/en-en/news/2019/jun/news- 3340185.html AI hit the roads in 2019: https://www.volvogroup.com/en-en/news/portraits/ai-hits-the-road.html volvos first self driving car: https://www.engadget.com/2019-06-12-volvo-and-uber-self-driving-production- car.html?guccounter=1&guce_referrer=aHR0cHM6Ly93d3cuZ29vZ2xlLmNvbS8&guce_referrer_sig=AQAAAG5Fnb7IogE9u7P 2s64t6ii3qWFku7f7D5j1EpvUvaFCXwns8HQap4nqcQT0ftAJxiK21iBYOea5SgBym- WruaNu51AlEKsquvL51VtPqehdIooEuKJTB5cyaYx-e9aiDHgCaxgZpSxvDBsm3VbVjZ2FiTgeeOlJ2Oz4FiBTCpd9
WÄRTSILÄ OYJ ABP Industrials 2017 EUR in late 2019 they launched this product: https://www.wartsila.com/twentyfour7/innovation/wartsila-s-next-generation-maintenance- platform also in 2018 they write that they are using AI to provide expertise: https://www.wartsila.com/twentyfour7/in-detail/ai-makes- experts-more-curious-and-proactive but also this in small scale 2017: https://www.wartsila.com/media/news/11-10-2017-wartsila-uses-start-up-mindset-to-accelerate- intelligent-vessel-outcomes R&D about AI related system in 2016, but not yet implemented i guess: https://www.wartsila.com/kor/en/media/news/26-09-2016- wartsila-to-participate-in-research-programme-aimed-at-creating-an-ecosystem-for-autonomous-marine-transport dedication to AI & digitalization start in late 2016 and 2017 https://www.wartsila.com/media/news/29-05-2017-wartsila- introduces-dedicated-organisation-to-drive-digital-transformation
YIT Oyj Industrials 2018 EUR
Are using AI in road maintence since 2018 https://www.yitgroup.com/en/news-repository/news/cities-are-first-built-virtually In the first article that could be found its said “has been developed for a couple of years, and it is already used on a daily basis across Finland.”
ÅF Pöyry B Industrials 2018 SEK
In 2019 they have their own AI product called flowity. Also chatbot development for a hospital. Also used AI to streamline major energy customer in 2018. https://www.afconsult.com/da/newsroom/news/#2018 but cannot find anything before 2018.
Ørsted Ulilities 2018 DKK
First mention of AI in 2018: https://orstedcdn.azureedge.net/- /media/Annual_2018/Orsted_Annual_report_2018.ashx?la=en&rev=cec43e106d9a4ca58e0e3cffc8c3841c&hash=8B79943076695 EEBF901C27F2A4C28BB This was also the year they significantly scaled up their digitalization efforts. so maybe they have better financial performance 2018 and 2019 than they had before. Uses AI in 2019 https://www.technologyrecord.com/Article/216rsted-chooses-microsoft-advanced-analytics-ai-and-the-cloud- 76614 & https://orstedcdn.azureedge.net/-/media/annual2019/Annual-report- 2019.ashx?la=en&rev=334895b2e83e4266afb7e97cfa9024f2&hash=BA390050EDD075C9C7E514CF02BB8D6F
Össur Health care 2010 DKK commercial release of AI powered knee in 2010. https://corporate.ossur.com/corporate/newslist/782-next-generation-of-the-power- knee-in-early-release-at-walter-reed-army-medical-center They have AI still in. In 2019, they announced new next gen foot intelligent prosthesis. https://assets.ossur.com/library/40582
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