1
Does the level of digitalization positively
influence firm’s corporate performances?
Mischa Hart Nibbrig
10679367
Bachelor Thesis – Final Version
26.06.2018
BSc. Economics & Business
University of Amsterdam
Supervisor: Dr. Andreas Alexiou
2 Statement of Originality
This document is written by Student Mischa Hart Nibbrig, who declares to take full responsibility for the contents of this document.
I declare that the text and the work presented in this document are original and that no sources other than those mentioned in the text and its references have been used in creating it.
The Faculty of Economics and Business is responsible solely for the supervision of completion of the work, not for the contents.
3 Abstract:
This study investigates the direct relationship between the level of digitalization in use by a firm and its subsequent corporate performance; and argues that the level of innovation adopted positively moderates this. relationship. Using a sample of 90 companies with data from 2017, significant empirical results
confirm a positive relationship between digitalization-related investments and an increase in annual revenues but not with the value of market capitalization. Empirical results also indicate a minor moderating influence by the level of innovation on market capitalization but not on annual revenues.
4 TABLE OF CONTENTS
1. INTRODUCTION 5
2. LITERATURE REVIEW & HYPOTHESES DEVELOPMENT 6
2.1) Literature Review 6
2.1.1) Digitalization 6
2.1.2) The Importance of Digitalization 7
2.1.3) Digitalization vs Innovation 9
2.2) Research Question 11
2.3) Theoretical Framework 12
3. METHODOLOGY & RESULTS 13
3.1) Data Collection, Sample & Variables 13
3.1.1) Dependent variables 14 3.1.2) Independent variables 14 3.1.3) Moderator variable 15 3.1.4) Control variable 15 3.2) Data Analysis 16 3.3) Results 17 4. DISCUSSION 21 4.1) Theoretical Implications 21 4.2) Managerial Implications 22 4.3) Limitations 22 5. CONCLUSION 23 BIBLIOGRAPHY 25 APENDIX 31 LIST OF TABLES
Table 1: Summary of Variables 15
Table 2: Descriptive Statistics and Correlation of our variables 16
Table 3:Regression Results (DV: Annual Revenues) 18
Table 4: Regression Results (DV: Market Capitalization) 19
LIST OF FIGURES
Figure 1: Conceptual Model 12
5 1. INTRODUCTION
Leading consulting companies, renown business leaders, and academic researchers, all seem to acknowledge that Digitalization is one the most significant on-going developments of contemporary society today, generating changes with respect to business and private life. This comes as no surprise, since more people today have access to a mobile phone than to electricity (ITU, 2017), and the overall number of internet users worldwide was recorded at more than 3.5 billion single humans in 2017 (Statista, 2017). In fact, this new global digitalization ‘upsurge’’ that one can experience today, has been considered by some, as the fifth Kondratiev wave after the steam, steel, electricity, and petrochemical revolutions (Vogelsang, 2010).
From a business perspective, Digitalization can be defined as the “use of digital technologies to change a business model and provide new revenue and value producing opportunities” (Gartner, 2017a). This transformation is considered by numerous authors and consulting companies as a major driver of
organizational efficiency. Not only does digitalization improve physical assets & capabilities leading to better analytics, increased mobility and smarter devices (Routley et al, 2013, Yoo., 2010); but it also
develops a subsequent change in business integration which creates the true organizational value (Campbell., Peppard., 2007) . Continuing on this line, existing academic literature have demonstrated that digitalization leads to meaningful decreases in costs (Mitra., Chaya., 2015) and has created considerable new
opportunities to connect with customers and open new market opportunities (Gunday et al., 2011, Anderson, et al., 2012)
Yet it seems that in the Academic literature, there is a shortcoming of research of the direct effect of digitalization on corporate performance measurements. Therefore the aim of this thesis is to contribute to the existing literature about this relationship, and empirically examine whether the approach of increasing digital technologies within the organization strengthens its corporate performances. Hence, this thesis’ main research question will be: Does the level of digitalization positively influence a firm’s corporate
performances?
The main focus of this thesis lies on the relationship between the level of Digitalization in use by an organization and the consequential performance results. In order to assess and quantify the corporate performances of firms, this thesis will therefore look at two distinct financial measurements, namely the Annual Total Revenues and the value of Market Capitalization. This thesis also examines whether the level of innovation adopted by the organization moderates the aforesaid relationship. To come to conclusions, empirical analysis will be performed by applying various regression analyses on the basis of data acquired from accounting firm PricewaterhouseCoopers, and extracted from multiple Annual reports. Altogether this thesis supports existing research and addresses the above-mentioned shortcoming with additional
6 The remainder of this thesis is structured as follows. Next section contains a thorough rundown of academic literature, insights of major consulting companies and statements business leaders on the importance of Digitalization and its connection with Innovation. Subsequently, the core research question and consistent hypotheses of this research will be formulated and illustrated. The third section provides information about data collection followed by an analyzation of the results. The fourth section discusses the theoretical and managerial implications with respect to the results and outlines the limitations of this research. Finally the fifth section consists of a conclusion.
2. LITERATURE REVIEW & HYPOTHESES DEVELOPMENT
The purpose of this thesis is to analyse to what extent digitalization has an impact on a firm’s corporate performances. Therefore, in this section, a thorough analysis based on academic literature, insights and propositions of major consulting companies and statements by business leaders on the topic will be conducted. Subsequently, the core research question of this thesis will be proposed and consistent hypothesis based on the literature review will be formulated. Hence, we will first try to grasp the most appropriate definition of digitalization, next we will try to understand why the digitalization process of companies is important today; wherefore advantages and disadvantages will be identified. And finally, as part of our research, we will look at the combination of innovation and digitalization, and to which extent both developments benefit and support each other.
2.1) Literature Review
2.1.1) Digitalization
The first modern use of the term “digitalization’ with respect to computerization was found in an essay published by the North American Review in 1971 (Brennen, Kreiss, 2014). Therein, Wachal (1974:575) examines the social implications of the “digitalization of society” considering the advantages and
disadvantages for computer-assisted humanities research. In the last decade the terms digitalization and digitization have really taken off and have often been used by authors conversely (Fodor, 2017) , yet
depending on the discipline and background of the authors, their meanings seems to slightly differ (Brennen, et al., 2014; Gartner, 2017; Park, Saraf, 2016). In the paper by Brennen and Kreiss (2016: 556) however, a clear distinction between the two appellations is proposed. The authors define digitalization as “the way many domains of social life are restructured around digital communication and media infrastructure”, while digitization is interpreted as “the material process of converting analog streams of information into digital bids 1 & 0”. Following the lines of thoughts of the first definition, distinct interpretations of the term
7 digitalization have been made from a business and organizational management approach. For instance, according to Park, & Saraf (2016) , digitalization is a sophisticated process that contains transformations not only in IT but in overall organizational strategies due to the changes in business processes, organizational knowledge, new IT implementation, which subsequently influences organizational performance. In the Gartner IT Glossary, which is the world's leading information technology research and advisory company (Gartner, 2017, Fodor, 2017) another similar business-related concept for the term digitalization is defined. In there, digitalization is defined as “the use of digital technologies wherefore changes in business models are made and [in turn] provide new revenue and value-producing opportunities; it is the process of moving to a digital business” (Gartner, 2017). For the sake of clarity, when using the term digitalization in this thesis, I will refer to the business-related definitions used by the paper of Park et al. (2016) and the Gartner (2017a) IT Glossary.
2.1.2) The importance of Digitalization
According to the International Telecommunication Union (2017), more people today have access to a mobile phone than to electricity, powering exponential growth in global data generation. In fact, since 2014, analysts proclaim that there are more mobile devices than people in the world (Independent, 2014). And it doesn’t stop here; according to online German statistics database Statista (2018), the global mobile population today amounts for more than 3,7 billion unique users, mobile devices account for 49.7 percent of web page views worldwide and the overall number of internet users worldwide is recorded at more than 3.5 billion single humans in 2017. In his article “Digitalization in Open Economies” (2010) , Vogelsang suggests that this new global digitalization ‘upsurge’ that we experience today, can be considered as the fifth
Kondratiev wave after the steam, steel, electricity, and petrochemical revolutions (Fodor, 2017). Together with globalization, this new digital wave has accelerated the rhythm of technological change (Vasconcelos, Kimble, & Rocha, 2016) introducing new disruptive technological systems such as artificial intelligence, big data, robotics and nanotechnology. Yes, digitalization seems to be one the most significant on-going
changes of contemporary society today (Hagberg & Sundstrom & Egels-Zandén, 2016), which consequently incorporates many elements of business for individuals and organizations. Undeniably, over the past decade, enterprises, corporations, SME’s and big business entities have all experienced this new digital wave and have been affected by it positively or negatively. (Heckman &Kautz, 2012; Heckman, Stixrud, & Urzúa, 2006; Kuhn & Weinberger,2005; Weinberger, 2014; Brynjolfsson & McAfee, 2014). If we believe the theory of adaptability by Martin Reeves, the managing director of the Boston Consultancy Group (2015) who states that ‘’to adapt, a company must have its antennae tuned to signals of change from the external environment […] and constantly act to refine or reinvent its business model”, one can assume that it is essential for companies to keep up with this modern digitalization wave. Hence, rreferring to Ritzer & Jurgenson (2010), these professors argue that the global market has always been ‘outlined by prosumption’, yet, ‘digitalization
8 involves important changes in the different means for which production and consumption link together’. Specifically, digitalization creates a great deal of new opportunities to connect customers and employees, and establishes a ‘blurring of their boundaries’ (Ritzer and Jurgenson, 2010). This assumption is supported by Akerman, Gaarder & Mogstad (2015), in their work they argue that digitalization causes new circumstances by which many companies have difficulties to manage; such as “cultural diversity in a global marketplace, new and emerging customer segments, market volatility, raised customer expectations about quality of products and services, and the impact of the internet on an organization's core business” (Akerman, Gaarder, & Mogstad, 2015; Markowitsch et al., 2001; Sousa, & Rocha, 2018). But with the help of new digital technologies, organizations can now create better user and consumer communities, which subsequently contributes to improved brand building and modern ecommerce channels (Haegeman, Marinelli, Scapolo, Ricci, & Sokolov, 2013). Overall one can argue that the concept of digitalization has reached indisputable level of importance for the global market and organizations. In fact, as reported by a survey published in the ‘’Digital Transformation in the Age of the Customer”’ (2015), only 5% of organizations deem that they have mastered digitalization within their firm to the point that they stand above their competitors. Likewise, surveys conducted by MIT and Deloitte reveal that 76% of their respondents believe that digital technologies are crucial for their organizations today, whereby 92% reported that it will be imperative in the next 5 years (Kane, et al., 2015). Finally, to give an illustration, the growing awareness of this new digital wave is also justified by the scientific community and its academic literature. Namely more than 80% of studies about digitalization were published between 2010 and 2016 (Kahre, et al., 2017, Fodor, 2017). Once again, these findings underline the attention of digitalization for organizations today and in the future.
In light of this, it appears some authors argue against the concept of organisations increasing their digital technologies such as IT infrastructure or software and proclaim that the concept of digitalization can in turn affect firm’s performance negatively. Indeed, Lucas & Goh (2009) concluded from their research about disruptive technologies, that, digitalization can lead to significant positive performance results, but digital transformation can also develop profoundly complex innovation challenges, and if organizations fail to undertake them accurately, organizations can endure major losses. Reasons for companies to be
confronted with substantial barriers to digitalization are for example: a lack of top management support or a lack of internal workforce with sufficient technical capabilities (Chircu, Kauffman, 2000). Some authors go even deeper such as American writer Nicholas Carr (2013) who considers that investing in Information Technology – such as software – is overrated and leads to the destruction of a company’s value. He claims that IT-investments are only leading to ubiquity and is not the main engine of competitive advantage, scarcity is. Hence, IT capabilities are likely to suffer from commoditization and are not a sustainable competitive advantage for most organizations.
Nevertheless, many authors, academic researchers, consulting offices and business leaders advocate in favour of digitalization. very major firm in the consulting industry, such as Deloitte (2017), Accenture (2017), McKinsey&Company (2017), and The Boston Consulting Group (2017) place an important emphasis on advising their clients in digitalizing their own companies and updating them with the latest IT business
9 solutions, findings, predictions and knowledge (Fodor, 2017). Accordingly, this appreciation with respect to digitalization is validated by different academic researches. In his studies, Bharadwaj (2000) did an empirical analysis which shows an association between superior IT capabilities and superior firm’s performance which is positive and significant. Likewise, Kim, Shin, Kim, & Lee (2011) have also identified a positive effect of IT capabilities on financial performance. And so, did Weill and Woerner (2013), who claim in their study that firms with above-average levels of digital revenue demonstrate a 1.5 % faster growth-rate on average than the industry mean. But more importantly, what are explanations for such increases in performance? According to Campbell & Peppard (2007), digitalization not only consists of improved physical assets & capabilities but more importantly develops a subsequent change in business integration which creates the true organizational value. Digital integration implies the use of better analytics, increased mobility, better use of social media for internal and external purposes, and the use of smart-embedded devices into the core businesses (Routley et al., 2013). Hence new methods for brand-building, marketing, and sales are enabled, which empoweres organizations to gain better insights into customer behaviour and demand (Business Sweden, 2016, Fodor , 2017). Digitalization also leads to decreases in costs. Mitra and Chaya (2015) present in their empirical study that higher investments in IT are generally related with lower production costs and lower total operation costs as it increases business intelligence overall. Respectively one can argue that companies with lower costs can invest more in other assets or lower the prices of their products/services and therefore have more competitive advantage (Andersson, Johansson, Karlsson, & L f, 2012). Finally, as reported by major consulting companies, in parallel with constant increasing global innovations, digitalization forges remarkable new business developments which will be discussed in the next part. Overall, we can assume that digitalization truly impacts an organization, and by analysing all the previous researches it seems as if the use of digital technologies improves business performances.
Accordingly Digitalization with regard to Corporate Organisations will be the core independent variable of this thesis and the focal point of the first hypothesis. But first, in the next part of this paper we will
investigate whether the level of innovation adopted by the an organization has an influence on the level of digitalization.
2.1.3) Digitalization vs Innovation
From a business prospective, what is innovation? Acclaimed management professor Peter Drucker interprets innovation (1981) as a change that creates new dimensions of performance. Baregheh, Rowley & Sambrook (2009) go on by defining innovation as a ‘multi-stage process whereby organizations transform ideas into new and improved products, service or processes, in order to advance, compete and differentiate themselves successfully in their marketplace.” It is true that innovation is widely considered to be a significant force of competitiveness, which can be traced back in the processes, services, products, philosophies and organizational structures of a company (Gunday, Ulusoy, Kilic, & Alpkan, 2011). Koa
10 (2015) describes innovation as ‘the set of capabilities […] that allows the continuous realization of a desired future by transforming, with the help of new research findings and technologies, what is possible into what is valuable for many’ (Kao, 2015). From this point forward, innovation can open new market opportunities and potentially revolutionize organization’s habits (Gunday et al., 2011; Botkin, 1999). Since it has been proven that organizations engage in innovation can boost their competitive advantage and consequently increase their growth (Baregheh et al., 2009; Ruttan, 2000) , one can assume that the level of innovation use by firms would have an effect on digital-related investments and the process of digital transformation. This
assumption has been evaluated thoroughly by Yoo (2010). In her studies she demonstrates that thanks to this new global digital burst, digital technologies have become pervasive (Yoo, 2010) creating innovations that are characterized by convergence and generativity. Nowadays, thanks to digitization, technologies are not tangible and immutable anymore (Zittrain, 2006), but instead are easily adjustable, modular and can be combined with multiple different technologies, objects or situations (Zittrain, 2006; Yoo, 2010; Anderson, 2006). For instance, the concept known as "triple-play" which combines, phone, broadband Internet and TV cable; or even "quadruple-play" which consists of including mobile Internet, are direct outcomes of
pervasive digital technology and bring together new inventions and tools that revolutionize the routines of individuals and organizations (Yoo, Boland, Lyytinen, & Majchrzak, 2012). Think of tablets or mobile phones. Another example is the emergence of ‘platforms’. Gawer (2009) describes platforms as ‘building blocks that provide an essential function to technological systems and which act as the foundation upon which other parties can develop complementary products, technologies or services" (Gawer 2009, p. 2). Platforms have become the central focus of many organization’s innovation activities (Anderson, 2006; Brynjolfsson, 2010; Gawer, 2009). And it doesn’t stop here, the combination of pervasive digital innovations with sustained improvements of computing and the ability to stock tremendous amounts of digitized information (also known as big-data) are transforming the operationalization systems of most big companies nowadays (Brynjolfsson & Mcafee, 2017). According to major consulting company Accenture (2018), companies are now using ‘self-evolution systems’ which are intelligent machines that are able to adapt and self-evolve. These machines make use of computational procedures basing their conclusions on previous applications, as a means of obtaining successively closer approximations to the solution of a problem. These new technologies can be used in many facets of a company. Edge Centricity is another result of innovation and digitalization (Deloitte, 2017). Edge centricity enables big companies with an important amount of locations around the world to empower their local managers with local decision making since these managers now have easy and fast access to terabytes of data about the company. This increases visible-customer leadership, empowers the frontline and engages the Backoffice (Deloitte, 2017).
It is clear now that pursuing digital transformation can create both opportunities but also challenges for companies. We have seen that today’s digital innovations are improving at a significant rapid pace (Yoo, Boland, Lyytinen, & Majchrzak, 2012) thanks to the ability for technologies to be reconfigured (Yoo, 2010; Tiwana, 2010). Yet as Nylen & Homstrom (2015) demonstrate in their studies, the rapidity of pervasive digital innovations becomes distinctly challenging when organizations invest in the design of “smart”
11 products that enclose digital elements in traditional tangible objects and systems (Nylen, Holmstrom, 2015). Companies that invest in R&D and IT-solutions need to realise that digital technologies are constantly evolving towards lower production costs and greater processing capacities (Yoo et al., 2010). As reported in the article by Westerman (2016), organizations should not overuse digitalization, as he states: “The right amounts, applied under the right conditions, can lead to fabulous results.” Yet depending too much on automation can indirectly decrease the quality of the service or product, and potentially hinder employees’ loyalty to one’s company which is crucial for further innovation and growth (Shermon, 2016)
Overall one can conclude that digitalization and innovation can both separately and mutually help an organization grow faster and undoubtedly increase its corporate performances. In this respect, the level of innovation will be the underlying moderating variable of our research about digitalization and corporate performances and subject to the second hypothesis. Hence in the next part of this section, we will outline a research gap, present the leading research question of this thesis, and clarify the subsequent hypotheses.
2.2) Research Question
So far a detailed rundown on the importance of Digitalization for an organization and its association with Innovation were presented. Different authors from academic, business and corporate background have suggested theories and outcomes as a result of these two subjects. From their insights we have learned that digitalization is an omnipresent evolution in today’s world (Statista, 2018; Hagberg, et al., 2016; Heckman, et al., 2010). We have understood that increases of IT capabilities can have positive impact on financial performances (Kim, et al., 2015); and are generally related with lower operation costs which can lead to competitive advantage (Mitra, et al., 2015; Andersson, et al., 2012). We have also learned that innovation can open new market opportunities (Gunday et al., 2011), and that it is substantially associated with digitalization (Yoo, 2010; Tiwana, 2010; Gawer, 2009).
Yet it seems that in the Academic literature, there is a shortcoming of research of the direct effect of digitalization on corporate performance measurements. Even though major consulting companies have been advocating firms to invest in digitalization promising enhanced results, these same companies never clearly show, publicly, the way they come to those conclusions in terms of quantitative data.
For that reason, the objective of this thesis is to empirically examine whether the approach of increasing digital technologies within the organization strengthens its corporate performances. Hence the research question of this thesis is:
12 2.3) Theoretical Framework
In order to assess and quantify the corporate performances of firms, we decide to look at two distinct financial measurements. The first measurement that we will evaluate is the Annual Total Sales (also known as Revenue) reported by the company at the end of the year. The second measurement that we will evaluate is the value of the Market Capitalization estimated during the last week of that same year . By doing so we initiate two distinct volumes that will be our dependant variables.
These two dependent variables will be subject to our core independent variable, namely Digitalization. Following the theories and thoughts outlined throughout the literature review this thesis expects the level of digitalization to positively influence the corporate performances of the organization. Henceforth this thesis is based on the following two hypotheses:
Hypothesis 1a: Firm’s level of Digitalization is positively associated to its Total Annual Sales Hypothesis 1b: Firm’s level of Digitalization is positively associated to its Market Capitalization
To divulge more into the outcomes of our literature review, and to evaluate whether Innovation truly drives digitalization ( or vice-versa). This thesis expects the ratio of innovation to positively moderate the influence between Digitalization and Corporate Performances. In other words, our results should show even greater corporate performance measurements. Therefore we will include the ratio of Innovation in use by the firm as a moderating factor, which brings about two additional hypothesises:
Hypothesis 2a: Firm’s Level of Innovation positively moderates the relationship of Hypothesis 1a Hypothesis 2b: Firm’s Level of Innovation positively moderates the relationship of Hypothesis 1b
Finally the following conceptual model is constructed to illustrate the network between the research question and the hypothesises:
13 3) METHODOLOGY & RESULTS
3.1) Data Collection, Sample & Variables
In order to effectuate the empirical analysis of this paper, secondary data is collected and used from different databases. This data has been decomposed and adjusted to achieve the objective of this research, namely to test whether the level of digitalization positively influences corporate performances. The purpose of this thesis is also to enhance and increase the statistical knowledge and conditions for further scientific research about this same topic.
In this regard, the first source of our data originates from the multinational accounting company PricewaterhouseCoopers (PwC). This data set, named The Global Innovation 1000 study, analyses spending of the world's 1000 largest publicly listed corporate R&D spenders during last fiscal year 2017 (Strategy&, 2017). According to the document, ‘subsidiaries that were more than 50 percent owned by a single corporate parent during the period were excluded if their financial results were included in the parent company’s financials’. To come to these results, PwC obtained the key financial metrics for 2012 through 2017 such as sales, gross profit, operating profit, net profit, historical R&D expenditures, and market capitalization from Bloomberg and Capital IQ which are two major financial software and databases (Strategy&, 2017). Next, all PwC converted all the Sales and R&D expenditure figures from the original foreign currencies to U.S. dollars corresponding to the mean exchange rate over that particular period (Strategy&, 2017). For the data on share prices, PwC used the exchange rate on the last day of the period. And finally, all companies were coded into one of forty-five industry sectors according to Capital IQ’s industry designations (Strategy&, 2017). The second source of data was extracted by myself from more than 90 different Annual Reports of 2017. These 90 Annual Reports represent 90 different public companies from different parts of the world that are also part of the first source. Finally the third source of data was extracted from Ycharts which is
prominent financial data research online platform.
Since all companies were coded into forty-five different industry sectors, companies with industries: Computer-telecommunication, Aerospace/defense, Electronic Equipment, IT Services, Pharmaceuticals, Power Supply, Cable & Satellite, Biotechnological, & Software/Gaming were filtered out of the dataset. Since these industries incorporate high concentration of high-tech industries (Florida, Gates, 2003) , this was done to avoid biasness during the tests. Next, to find the exact amount of Software Asset invested in 2017, out of 386 Annual Reports that were left, only 90 Reports distinctly published these amounts. Hence the data was filtered and reduced to a sample of 90 companies which are used for empirical analysis. Hereunder follows a detailed outline of our variables.
14
3.1.1) Dependent Variables
There are numerous ways to measure corporate performances of companies (Hagel, Seely Brown, Davison, 2010) such as focussing on Return on Equity (ROE) or techniques like the Internal Rate of Return (IRR), Cash Flow Return on Investments (CFROI) or Discounted Cash Flow (DCF). However, firms can make use of financial methods to artificially sustain a strong ROE (Hagel, et al., 2010), and the other three methods tend to be very complicated when trying to acquire sufficient data. Therefore, during this thesis we adopt for more straightforward means of measurements which are the Total Annual Sales and Market Capitalization.
In fact, according to a survey of 400 financial executives by Graham, Harbey & Rajgopal (2005) from Standford Graduate School of Business, nearly two-thirds opted for Annual Sales as one of their favourite mean of measuring growth (Mauboussin, 2012). Hence to acquire the Total Annual Sales of 2017 for our sample tests, we make use of the first source which entails the data collected by PwC. As for the value of Market Capitalization, it is used by many organizations such as the World Bank (Bayraktar, 2014) since it is one of the most useful methods to weigh growth considering the ease of the calculation with data availability (Svanadze, Kowalewska, 2017). Market capitalization ‘refers to the total market value of a company's outstanding shares and is calculated by multiplying a company's shares outstanding by the current market price of one share’ (Investopedia, 2017). For this, the data was acquired manually from Ycharts, an online financial data research platform. For every 90 companies the exact value of their Market
Capitalization in the last week (52) of the year 2017 was extracted from historical records.
3.1.2) Independent Variable
Our independent variable represents the level of Digitalization in use by the firm. As mentioned earlier, According to Gartner IT Glossary, digitalization refers to the process of moving to a digital business by using digital technologies wherefore changes in business models are made” (Gartner, 2017). Since it is still a modern term, its definitions varies depending on the author and there is no official financial or accounting indicator designated to the concept, the level of digitization becomes a complex measurement to capture. However a few organizations have attempted to measure current digitalization within corporate organizations such as the Digital Density Index (DDI) by Oxford Economic and Accenture (Kotarba, 2017) which measures how digital technologies impact the economic growth of countries and firms (Macchi, et al., 2015). Or the Industry Digitalization Index by the McKinsey Global Institute (MGI) which covers three groups of metrics, namely assets, usage, and labor (Kotarba, 2017) which is derived from data collected by the Eurostat databases (Friedrich, et al., 2011). In this thesis, we make use of the second source which comprises 90 separate Annual Reports to calculate the level of Digitalization. By extracting the amount of “Software Asset” recorded in the Annual report (often labelled as part of the Intangible Assets), and dividing
15 this amount by the total Annual Assets – also recorded in that same Annual Report – one can get the
percentage spent in Software in terms of assets which is our closest representation of the level of Digitalization.
3.1.3) Moderator Variable
As a moderator, firms’ degree of innovation is included in my model. The level of innovation will be measured by R&D expenditures as a percentage of total sales which are both extracted from our first source by PwC.
3.1.4) Control Variable
Finally as a control variable we will include the Size of the Firm in our tests. The Size of the Firm is measured in terms of Full Time Equivalent which represents number of employed person within an
organization (Eurostat, 2010). We use this moderator variable since there is empirical evidence about the positive relationship between firm size and profitability (Shepherd, 1972, Lee, 2009) and about the relationship between the rapidity of adoption of new innovations with respect to firm size (Hong, Oxley, McCann, Le, 2016)
Table 1 outlines and the variables that will be used in our empirical analysis
Table 1: Summary of variables
Variable Operationalization
Dependent
Total Annual Sales Amount of Sales publicly reported
Market Capitalization Total share outstanding multiplied by market price of share
Independent
Degree of Digitalization Carrying amount of 'Software' Asset as a percentage of Total Assets
Moderator
Level of Innovation Total annual expenditure in R&D as a percentage of total annual revenues
Control
16 3.2) Data Analysis
The data is arranged and analysed through SPSS. It is important to mention that, along the upcoming regression analysis and subsequent results, this papers follows the approach & philosophies of Neyman and Fisher (1950, 1961, 2006) about the degree of significance; namely in this paper one can assume that a significance level α below p = 0.10 is statistically ‘acceptable’ for rejecting null hypotheses. Hence in this paper the results of interest will be labelled with appropriate asterisks (*) as mentioned below the respective tables. Considering this, in Table 2 one can recognize the descriptive statistics and correlations between the variables of our research. Based on the 90 multinational companies that we test, the average Full Time Equivalent of the sample – in other words, the average number of employees – is equal to 97.900. The table also tells us that the companies of our sample realize on average around 29.25 USD Billions of Revenues yearly and the mean value of their Market Capitalization is worth 40.91 USD Billions at the end of the year 2017. Further, with respect to the level of digitalization, the companies in the sample spend on average 2.59 percent of their Assets on Software. And according to the mean value of the variable Level of Innovation, these companies invested in 2017 around 3.75 percent of their Total Annual Revenues in Research and Development. Next, one can observe from Table 2 that there is strong correlation between Total Annual Revenues and Annual Market Capitalization; this makes sense since both are similar measures of
performance. Another strong correlation appears between the Firm’s Size and Total Annual Revenue; again, this is realistic since many studies suggest a growth in size of the firm leads to higher financial performances (Kim, et al., 2015). Interestingly, table2 reveals that the Level of Innovation in use by the firm shows a negative correlation factor with the two performance measurements; which would contradict many studies arguing that innovation is a source of performance. Nonetheless, the level of Innovation does have a
significant positive correlation strength with the Level of Innovation which reflects the arguments in part 2.3 of the Literature Review.
As regards to multicollinearity, besides the strong correlation between our two dependent variables – which will not damage our results since these are used in difference tests – all other interactions do not indicate risk of multicollinearity. To be certain, we also verify this by the means of a Variance Inflation Factors (VIFs) analysis. All VIF values are around 1.065 according to SPSS which is below 3.000 as advised
17 by Craney & Surles (2002). And by looking at the relevant Tolerance levels, SPSS identifies levels around 0.93 which are much higher than the critical values suggested by Field (2009) , namely below 0.2. See Appendix for the respective coefficient tables. All in all, there is no sign of multicollinearity.
When testing the normality of our sample, it appeared the data was not yet normally distributed. One reason was that the sample size is smaller than 200, namely 90, which according to Field (2013) can increase the risk of kurtosis and asymmetrical skewness. By carrying out a boxplot, one could clearly notice the high percentage of outliers which basically referred to companies such as Volkswagen, Symrize AG, Procter & Gamble and others, whom investment in Digitalization is much higher than average. For that reason we made use of the log transformation technique (Changyong, 2017) and transformed all our data by computing it with arithmetic log10. As a result our sample conformed to normality according Kolmogorov-Smirnov and/or Shapiro-Wilk Tests, which in turn increases the validity of our statistical analyses further on. See Appendix for all normality tests.
3.3) Results
In this part of the thesis, two multiple linear regression analyse are conducted with the help of SPSS. This will enable us to answer our hypotheses and to determine if the chosen continuous dependent variables can be predicted from our group of independent variables. We appointed two dependent variables in the conceptual model namely Total Annual Revenues and Market Capitalization. Henceforth, a regression is conducted for both, and their respective results are displayed in Table 3 and Table 4. Both tables consist of 3 models. The control variable ‘Size of the Firm (FTE)’ only is applied in Model 1. The independent variable ‘Level of Digitalization is added in Model 2. And the moderating variable ‘Level of Innovation’ and its interaction variable ‘firm innovation level x level of digitalization’ are included in Model 3 aggregating a full model. This analysis follows the lines recommended by Pedhazur & Schmelkin (1991) which entails
considering the moderating variable together with the mean-centered values of the independent leading to interaction variables that are added to the full model.
The first interesting values are the p-values which are equal to 0.000 for all models in both Table 3 and Table 4. This verifies the validity of the models. From that, one can reject the alternative that none of the variables are responsible for the outcome of the dependent variables. Next, The R^2 and Adjusted R^2 values in both tables imply that the full models displayed by Model 3 clarifies almost the same low
percentage of data variability as in Model 1 and Model 2, since the values do not, or only slightly change in both regressions. This also confirms the outcomes by our correlation matrix (Table 2) which suggest that there is no significant relationship between the independent and dependent (0.132) and the moderating and dependent variables (-0.153).
18 Table 3 displays results from the regression model with dependent variable ‘ Total Annual Revenue’ which enables us to answer Hypotheses 1a and 2a.
In Model 2, Table 3, the estimate for the level of Digitalization shows a very low positive coefficient value (0.142), and its p-value (p = 0.128) does not support the fact that this would be a statistically
significant coefficient. In other words, this implies that there is not enough significant linear positive relationship between the degree of digital investments by a firm and its subsequent Total Annual Revenues. On the other hand, the results of Model 3 do reveal a significant (yet minor) direct effect of digitalization on Total Annual Revenues (0.186). Thus, one can appraise incremental validity – validity that seeks to answer if a new test adds more information than might be obtained with an already existing methods (Sackett, Lievens, 2008) – from this regression. Thereby, along the models, the R2 only slightly changes from 0.237 to 0.257 to 0.275 which indicates that the explanatory power of the full model is mostly strengthened by the control and independent variables. The findings presented in Model 3, reveal a significant (p=0.064) direct effect of digitalization (0.186) on total annual sales when adding the moderating variable ‘level of digitalization, which consequently supports Hypothesis 1a. Nonetheless, because the effect size is very low (0.186) and Table 3: Regression Results (DV: Annual
Revenues)
Model 1 Model 2 Model 3
Variables B SE B β B SE B β B SE B β
Control Variable
Firm Size (FTE) 0.00008 0.000 0.487*** 0.00008 0.000 0.490*** 0.00008 0.000 0.475***
Independent Variable Level of Digitalization 141.374 92.030 0.142 184.864 98.359 0.186** Moderator Variable Level of Innovation -166.633 127.306 -0.128 Interaction Level of Innovation x Level of Digitalization -1.636 4.045 -0.040 Constant 20.933 3.952 17.210 22.986 6.345 R2 0.237 0.257 0.275 Adjusted R2 0.228 0.240 0.241 F-stat 27.315 15.049 8.060 P-value 0.000 0.000 0.000 *p <0.10 **p <0.05 ***p <0.001
19 because the its significance level lays between 0.05 & 0.10 alfa, it is debatable whether this information is relevant in practice. The significant (p = 0.194) coefficient of the variable Level of Innovation in model 3 does not support Hypothesis 2a about firms’ innovation level positively moderating the relationship between the degree of digitalization and Total Annual Revenues. And at last but least, the control variable ‘Size of Firm’ is significantly related with the dependent variable Total Annual Revenues in all three models. In every model, for each 1000 employees added to the company, the corporate performance would grow with 0.08 percent.
Table 3 displays results from the regression model with dependent variable ‘Market Capitalization’ which enables us to answer Hypotheses 1b and 2b.
Table 4: Regression Results (DV: Market Capitalization)
Model 1 Model 2 Model 3
Variables B SE B β B SE B β B SE B β
Control Variable
Firm Size (FTE) 0.00003 0.000 0.172 0.00003 0.000 0.175* 0.00003 0.000 0.156
Independent Variable Level of Digitalization 187.967 127.263 0.154 293.723 133.770 0.241*** Moderator Variable Level of Innovation -320.339 172.727 -0.200** Interaction Level of Innovation x Level of Digitalization -6.077 5.488 -0.121 Constant 37.312 5.46 32.363 43.548 8.608 R2 0.03 0.053 0.275 Adjusted R2 0.18 0.031 0.241 F-stat 2.675 2.446 8.060 P-value 0.000 0.000 0.000 *p <0.10 **p <0.05 ***p <0.001
In Model 2, Table 4, the estimate for the level of Digitalization shows a very low positive coefficient value (0.154), and its p-value (p = 0.143) does not support the fact that this would be a statistically
20 significant coefficient. Again, , this implies that there is not enough significant linear positive relationship between the degree of digital investments by a firm and its subsequent Market Capitalization. However, the results of Model 3 do reveal a significant (p=0,03) direct effect of digitalization on Market Capitalization (0.241). In this case, the R2 levels do change with each model namely from 0.03 to 0.053 to 0.275. But so does the Adjusted R2 in Model 3, which increases by more than 0.2 indicating that the independent variable Digitalization has a strong effect on the dependent variable. These results support the Hypothesis H1b. Finally, the significant (p = 0.067) coefficient of the variable Level of Innovation in model 3 only partially supports Hypothesis 2b about firms’ innovation level positively moderating the relationship between the degree of digitalization and Market Capitalization. Interestingly, this is because the estimated coefficient is negative (-0.200), meaning that the moderating effect of the innovation level has an inverse power on the relationship between Digitalization and Market Capitalization than we expected. This is consistent with the theories of Nylen & Homstrom (2015). Contrarily to the previous regression with Total Annual Revenues as the dependent variable, the control variable Firm Size only shows a positive relationship with Market Capitalization (0.175) in Model 2 with a low significance level (p=0.097).
Regarding the interaction terms, table 3 tells us that there is no support for Hypothesis 2a since the coefficient is not significant (p = 0.194). However, in table 4, the moderating variable Level of Innovation does have an effect on the relationship between the independent and dependent variable; but surprisingly it shows a negative relation. While this does not support our hypothesis 2b, one could argue it is still an interesting interaction for this research. Hence hereunder Figure 2 illustrate the significant moderating effect of Model 3 in Table 3. From plain eye sight, the two lines representing either ‘Low or High innovation level’ seem to be parallel. Yet, if one would elongate these two lines, at some point there would be an intersection point where they would cross each other. This is due to the low negative power of the coefficient, namely -0.200.
21 4) DISCUSSION
4.1) Theoretical Implications
The importance of Digitalization in the academic literature, and especially in the Economics, Financial and Business Administrations literature is considerable. As mentioned earlier, more than 80% of studies about digitalization were published between 2010 and 20176 (Kahre, et al., 2017). While these findings underline the attention of this concept in the academic world, it seems there is a shortcoming of research of the direct effect of Digitalization on corporate performance measurements. As a result, this thesis contributes to the scientific literature by supporting existing research or academic theories, and by providing additional comprehensions and evidence about the direct effect of digitalization on explicit business measures such as Annuals Revenues, and the level of Market Capitalization. By that, since this thesis only uses data from the year 2017, one can argue that the results reflect the most contemporary numbers possible as of today. This thesis also thoroughly reviews the connection between Innovation and Digitalization, first from a literature perspective and subsequently through quantitative analyses.
From our regression models, empirical proof has been acquired and the results deduced that there is a relation between companies investing in digital-related (tangible and intangible) assets, and, a successive increase in their total annual sales/revenues. This insinuates that firms with digital investments might accumulate higher income or increase their earnings or both. Henceforth, these results are in line with the researches by Bharadwaj (2000) who found an association between superior IT capabilities and superior performances, and also confirm the academic observations made by Weill and Woerner (2013) between above-average levels of digital revenue and consequent faster growth rate. These results also support the interpretations by Mitra & Chaya (2015) who determined that higher investments in IT are generally related to lower production and operation costs, which in turn can increase competitive advantage (Andersson, Johansson, Karlsson, & L f, 2012). Nonetheless, the regression model with the same control variables was used to find its effects on Market Capitalization, but the outcomes were statistically insignificant. The most plausible reason for this lays in the data used. The sample only constituted of 90 companies and was subject of a high percentage of outliers.
With respect to the level of Innovation used by a company, and its moderating effect on the relationship between Digitalization on the value of the same company’s Market Capitalization, our regression model indicated significant results. These results demonstrated that the level of Innovation can truly positively enhance, or somewhat affect the performance of companies that invest in digital related assets . These result support the theories of Yoo & Zittrain (2010) about innovation technologies being more convergent and connected to digital infrastructure. And also approves with the studies of Anderson & Yoo (2006, 2010) considering that today’s pervasive digital innovations, a mixture between digital technologies and high levels of innovation, can enhance many aspects of a business. At last, these results also justify the
22 position Zhu, Dong, Xin Xu, & Kraemer (2006) on the power of innovation diffusion in combination with digital technologies. Nevertheless, results on the same moderating relationship on Total Annual Revenue were insignificant. Again this might be improved by changing and increasing the data sample; which turn can be appealing for further academic research.
4.2) Managerial Implications
The literature review and empirical results of this thesis can be of high importance in practice. Since our results have shown that there is a significant relationship between the level of Digitalization used within a firm and its total Annual Revenues, managers should not underestimate the power of investing in digital assets. While many major firms in the consulting industry have been advocating this for a longer time, the results of this thesis only reinforces their advises. Henceforth, this study might be interesting for companies that have not yet invested in modern software, and other IT infrastructure; or for managers that are still reluctant to the idea of more digital technology. However managers should understand that, just like Lucas & Goh (2009) concluded from their research about disruptive technologies, positive results from
digitalization do not only rely on digital infrastructure but also on the overall organizational mentality, processes that come with it, and the level of innovation that will be used. In other words, when managers are supervising and deciding on the appropriate equipment and machinery to invest in; the option of investing in high innovativeness assets might intensify the subsequent corporate performances. Indirectly these results also touch on different managerial aspects such the advantages of digitalization with respect to customer experience, logistics such as real-time decision making, and overall business processes.
4.3) Limitations
There are different limitations linked to this research. Eventually by recognizing these limitations, one can use them as opportunities for further research. The first limitation of this study relates to the term Digitalization which is also the core independent variable. Depending on the industry and background of the author, Digitalization can have slightly different meanings. It is important to take this into account in further research to avoid misunderstandings and divulge more in the same direction. Our second limitations
emphasizes the quality and quantity of our data. Since our sample is only n=90, in order to enrich the reliability of this research , one should collect a bigger sample in the future. This might enhance the level of significance of the regression outcomes and increase the level of normality of our data. This is not an easy task since many companies do not publish their exact numbers regarding the investments in software. The third limitation entails the quantification of the level of Innovation. In this research the level of innovation was evaluated as the Total Annual expenditure in R&D as a percentage of Total Annual Revenues. However
23 one could also use ‘the number of patents registered in the previous year’, ‘the total R&D headcount as a percentage of sales’, or even the ‘number of active projects’. Another limitation concerns the analysis and subsequent results, many outcomes did not achieve a very strong level of significance but were situated between or even above: ‘0.10 > P > 0.05’. For further research, besides increasing the n of our sample, one also could use more control variables such as ‘the age of the company’, ‘the type of industry’, or even the ‘geographical location of the firm’. This might increase the significance of the results but more importantly provide additional understandings into the relationship between Digitalization and Corporate performances. And finally, since Digitalization is still a modern concept, and because we only used date from the year 2017, I would recommend researchers to do a longitudinal study about its effects on performance to get better insights about the long-term growth prospect.
5. CONCLUSION
Scientific researchers, consulting companies and leading business figures all seem to perceive this upsurge of digitalization as one the most important on-going changes for organizations today. Yet
concerning firms, it seems that in the academic literature, there is a shortcoming of research of the direct effect of digitalization on corporate performance measurements. Therefore this thesis aimed at filling this gap by focussing on the research question: ‘Does the level of digitalization positively influence a firm’s corporate performances?’ with the level of Digitalization as independent variable. In order to divulge more extensively into the subject, this paper investigated the connection with Total Annual Revenues and Market Capitalization as representative performance measurements and framed both dependent variables in a conceptual model. This thesis also suggested and incorporated the Firm’s Level of Innovation as a positive moderating variable in the model. A a thorough rundown of literature was outlined about this subject and multiple regression analyses were conducted to test the hypotheses. The sample used was based on a dataset by PricewaterhouseCoopers, but also personally extracted from 90 different Annual reports and the financial data platform Ycharts. From the regression models, empirical proof deduced that there is a relation between companies investing in digital-related assets, and, a successive increase in their total annual sales/revenues, but the outcomes were statistically insignificant regarding the value of Market Capitalization. Similarly, empirical proof also indicated significant results concerning the moderating variable with respect to Market Capitalization but not on the relationship with Total Annual Revenue. Nevertheless these results moderately demonstrated that the level of Innovation can affect the performance of companies that invest in digital-related assets. Hence, this thesis contributes to the scientific literature by supporting existing research and by providing additional comprehensions. But also provides the practical world by suggesting that managers could increase their Digital-related investments if they want to achieve greater corporate performances. Finally, there are some limitations to this thesis that could be enhanced in further research; such as the
24 quality and quantity of data, the use of different interpretations of the Level of Innovation, the use of
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31 APPENDIX
32 Tests of the level of Collinearity:
