Master’s in Economics and Business

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Master’s in Economics and Business

Riding the wave of change: The financial performance effects of

digital transformation and the moderation of dynamic

environments

Kim Schoenmakers

S3741354

k.schoenmakers@student.rug.nl

University of Groningen

Faculty of Economics and Business MSc BA – Strategic Innovation Management

Supervisor: Assistant Professor M. Hanisch

Co-assessor: prof. dr. J. Surroca

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Abstract

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1. Introduction

Digital transformation, a change in how firms employ digital technologies to transform essential business products and processes, and organizational structures and management concepts, is getting increased attention in academic literature. Opportunities posed by digital transformation such as the change or creation of new business models (Lucas & Goh 2013; Schallmo et al., 2017; Verhoef et al., 2019) and transforming critical business operations (Gust et al., 2017) allows for new ways of value generation (Fitzgerald et al., 2013; Liere-Netheler et al., 2018; Vial, 2019; Gölzer & Fritzsche, 2017) and can result in enhanced financial performance (Pagani, 2013; Setia et al., 2013; Karimi & Walter, 2015). Attention to digital transformation and the underlying efforts have significant implications for strategic concerns such as behavioral theory (March & Simon, 1958), transaction costs (Williamson, 1975), organizational design (Simon, 1947), and technology evolution (Abernathy & Utterback, 1978). While research recognizes the development of new competitive advantages when paying attention to digital transformation (e.g., Verhoef et al., 2019), digital transformation efforts are not always successful (Tabrizi et al., 2019; Morgan, 2019). Only a few firms have positioned themselves to capture digital transformation's real business benefits (Westerman et al., 2012).

Despite the increased attention being paid to, and the advances in the emerging conversation about digital transformation, paradoxes exist about firms’ financial performance as a result of digital transformation. While some research argues that digital transformation reduces costs (e.g., Malon, 1987; Williamson, 1975; Aral & Weill, 2007), others have argued that digital transformation improves service quality (Bouwman et al., 2011) and allows for revenue increase (Setia et al., 2013). In other words, there is no comprehensive explanation of the financial performance effects and why these differ between companies.

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transformation efforts becomes crucial not only for capturing business profits from digital transformation but also to guarantee long-term financial health (Sebastian et al., 2017). Given these apparent paradoxes in extant research, I suggest that the effect of digital transformation on the firms’ performance is contingent on the environmental situation.

I test my theoretical arguments on a sample of 1037 firms by (1) quantitatively measuring digital transformation across different industry sectors, (2) examining financial performance effects over time, and (3) identifying environmental conditions moderating the effect of digital transformation on financial performance. Results presented significant revenue and income increase but do not show significant effects on cost reduction, meaning that digital transformation has a partial positive effect on firm financial performance. Environmental dynamism has a significant negative interaction effect on digital transformation's financial performance effects, meaning that the revenue and net income increasing effect of digital transformation are weakened. Environmental dynamism does not strengthen or weaken the cost-reducing effect of digital transformation.

This study provides an exciting opportunity to advance our knowledge of digital transformation by using a new theoretical perspective and a large dataset. First, the qualitative method offers an effective way of measuring performance effects over time, enabling managers to make better-informed decisions while transforming digitally. Second, I contribute to the literature on digital transformation and the attention-based view by better understanding the digital transformation performance effects based on managerial attention. This study can help managers make better-informed decisions based on the environmental changes in which the firm operates and their understanding of their actions.

2. Theoretical background and hypothesis development

2.1 Digital transformation and firm performance

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pervasive phase. Many researchers have analyzed different definitions to get to a comprehensive definition (Henriette et al., 2016; Reis et al., 2018; Verhoef et al., 2019; Vial, 2019), and they all emphasize the impact on the organization as a whole. For this research, I follow the definition of Verhoef et al. (2019) and Matt et al. (2015): “digital transformation is a change in how firms employ digital technologies to transform essential business products and processes, as well as organizational structures and management concepts, to develop sustainable competitive advantages that help to create and capture more value for the firm.”

To digitally transform, digital assets are required to effectively compete in the digital era (Verhoef et al., 2019) and which have the potential to enable a shift in business settings. Assets mentioned most in the digital transformation literature are big data (e.g., Vial, 2019; Verhoef et al., 2019; Hausberg et al., 2019), blockchain (e.g., Vial, 2019), and platform usage (e.g., Vial, 2019). One can describe big data as the collection and analysis of data that allows firms to offer services that better respond to customer needs and enable algorithmic decision making (Vial, 2019). Blockchain technology enables the creation of decentralized digital infrastructures (Tilson et al., 2010) to optimize processes such as supply chain processes. Using a platform to offer products or services, such as Netflix does, allows for digital communication opportunities and virtual networks (Parviainen et al., 2017) and, thereby, using more and new customers. Relating to these often-mentioned technologies, here defined as sub-clusters of digital transformation, are assets such as web-based sales, mass customization, the presence of a CDO, Internet of Things (IoT), and technologies such as RFID, augmented- and virtual reality, artificial intelligence, and machine learning. In this research, I define big data, process optimization, and platform usage as the sub-clusters of digital transformation consisting of several cluster assets (Appendix A).

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et al., 2015; Kiel et al., 2017); using artificial intelligence and blockchain logistic can optimize supply chain streams and reduce supply chain costs (Verhoef et al., 2019); and robots or virtual agents can replace costlier humans who also may positively affect the firm’s cost structure (Verhoef et al., 2019).

2.2 An attention-based perspective

The success of transforming depends on what is paid attention to and how this results in changes in process and operation management (Dremel et al., 2017). The original formulation of the attention-based view, first introduced by Ocasio (1997), has been shown to influence many strategic areas, including technology strategy (Eggers & Kaplan, 2009). The attention-based view of the firm defines attention as the process of noticing, encoding, interpreting, and focusing of time, energy, and effort by organizational decision-makers to make sense of the environment. Besides, it generally argues that the focus and distribution of managerial attention significantly influence organizational decisions, actions, and ultimately firm performance (Abebe, 2012). Whether digital transformation efforts result in a cost-reduction or revenue increase depends on the managerial attention paid to issues and their answers (Ocasio, 1997; Cho & Hambrick, 2006). Given that executives’ information-process cognitive capacity is limited (March and Simon, 1958), not all firm aspects receive equal attention. Aspects that attract a great deal of attention tend to influence organizational moves, strategy formulation (Hambrick & Mason, 1984), and performance (Abebe, 2012; Garg et al., 2003) significantly. It can, therefore, be argued that depending on what managers pay attention to, digital transformation efforts can be focussed on either cost reduction, revenue increase, or maybe both.

Overall, following prior research, digital transformation can lead to cost reduction or revenue increase. Expecting that companies continuously put effort into digital transformation, I predict that the long-term financial performance effects (e.g., 4 to 7 years) will be positive. I, therefore, argue the following:

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2.3 Environmental dynamism

Shifts in environmental conditions over time strongly influence organizational change (Romanelli and Tushman, 1986) and performance. By looking at environmental dynamism, complexity, diversity, and hostility (Mintzberg, 1979), you can assess the environment. As rapid change and uncertainty in an environment require quick responsiveness and adaptability, it makes sense that dynamism shows a more significant impact on innovation (Pérez-Lūno et al., 2014). Dynamism, being a considerable contingent dimension of the organization environment (Donaldson, 2001), reflects on instability (volatility) in the environment ((Keats and Hitt, 1988; Dess and Beard, 1984), technological change (Pérez-Lūno et al., 2014), and competition (Boyd et al., 1993). More specifically, Miliken (1987) describes environmental dynamism as the speed of product changes and the frequency of changing customer preferences. Knowing how to deal with environmental dynamism becomes crucial not only for capturing business profits from digital transformation but also to guarantee long-term financial health (Sebastian et al., 2017). It asks for flexibility and adaptability to respond to the opportunities and compensate for the threats posed by exogenous changes (Lawrence and Lorch, 1969; Davis et al., 2009). When firm success depends on flexibility and adaptability, environmental dynamism will likely affect both the productivity of digital transformation efforts and their ability to sustain the value appropriated from digital transformation. Furthermore, environmental change or the situation of the firm in the market drives the technology strategy (Birnbaum, 1984), defined as “the company’s approach to deploying technological resources to seize opportunities in the market and to counteract uncertainty” (Zahra & Covin, 1995, p.192).

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harder to anticipate future events and actions (Khandwalla, 1977). Therefore, increase uncertainty leads to an increased need for information gathering, processing, and adoption of information-processing mechanisms (Galbraith, 1973).

With digital transformation assets in place, managers can better process all the information and respond to the opportunities as they can directly apply the assets. For example, environmental dynamism's unpredictability on organizational outcomes provides more significant opportunities for the organization to exploit big data analytics capability (Chen et al., 2015). Their efforts may also provide insights about customer expressed and latent needs, which may result in the reshaping of strategies and increased revenues. Firms that can quickly respond to customer requirements may often realize long-term performance benefits (Vitari & Raguseo, 2016) because of increased net-income. Following Choe's (2003) findings, it is likely that in a competitive and uncertain environment, high levels of digital transformation efforts contribute more to an increase in financial performance than in a less uncertain or stable environment. This suggestion is in line with Lau et al. (2004) 's findings, who find that in a dynamic environment, a firm's capability to innovate in and maintain high-quality products or services correlated more positively with the firm's performance than in a stable environment. Focussing on cost reduction, Lau et al. (2004) found that a firms’ ability to reduce costs correlates more positively with its performance in a stable environment than in a dynamic environment. In a stable environment, when customer tastes and competitor strategies do not change, there is little reason to redesign or adapt products and services (Miller, 1988). Hence, they focus on cost-reducing activities. While prior research widely argues the ability of digital transformation to enhance efficiency and reduce operational expenses (e.g., Verhoef et al., 2019; Mithas et al., 2005; Brynjolfsson & Hitt, 2000; Malone, 1987; Williamson, 1975; Aral & Weill, 2007) a dynamic environment this effect will not strengthen this effect.

Following, I hypothesize:

H2a: Environmental dynamism positively moderates the relationship between digital transformation and firm financial performance, such that it strengthens the positive effect of digital transformation on revenue and net income increase.

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3. Methodology

3.1 Empirical setting

To give a comprehensive conclusion of the performance effects of digital transformation, I, together with a fellow student, my supervisor, and his colleague, created a dataset representing nine industry sectors. Using a cross-industry dataset is suitable for this research for two reasons. Firstly, digital transformation is present in all industries (Verhoef et al., 2019). Secondly, the effect of digital transformation may be different between industries depending on their environment. Therefore, it should also be possible to assess them individually and compare them to other industries. We choose to focus on the most valuable publicly traded companies in the U.S. as they tend to receive more public attention, which may be to our advantage when gathering data.

3.2 Data collection

For this research, I use a panel of 1037 American companies. Based on the Russel 1000 and Russel 3000 index, we pre-selected on performance. The large cross-industry sample of firms gives data on the years 2005 to 2019, and data were collected using 10-K filings, Annual Reports, Morningstar, LinkedIn, and the historical company website. The dataset consists of information about the industry, sector, firm patents, number of employees, financial data, shares and stock price, CEO educational background, and CEO earnings. The data was collected over 16 weeks by BSc students in the period April to May 2020 and September to October 2020. Following this, my student colleague and I cleaned the dataset and added missing information.

Next to this, to apply computer-automated text analysis (CATA) on company documents, the 10k filing per year per company has been saved as text files. I use these files to analyze attention patterns towards digital transformation assets. Especially in social science, researchers use this approach because the words individuals use embed the cognitive categories through which individuals pay attention (Sapir, 1944; Whorf, 1956). Because 10k filings contain financial information, I found it likely that digital transformation efforts aiming at improved financial results are mentioned in this report.

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Thesaurus. Third, following Beutel (2018) and Belderbos et al. (2017), we derived inductive terms from the text files by randomly checking 10k files across firms and years. We then added frequently used terms related to digital transformation to the dictionary, which resulted in a dictionary of 144 words. Fourth, as suggested by Krippendorff (2004), we performed a keyword-in-context (KWIC) analysis using the application MAXCDA, which allowed us to search for the keywords and view them in a tabular overview along with the words that appear before and after (their respective context). We applied the KWIC analysis on 457 randomly selected 10k files. Following the results of this analysis, we only included ambiguous keywords with an in-context occurrence share above 50% (Belderbos et al., 2017). We excluded 17 terms from the dictionary and added 189 more terms to the dictionary, which resulted in a dictionary consisting of 316 terms (Appendix B). Fifth, after using the dictionary to apply CATA on the 10k filings, we validate the dictionary by performing a factor analysis in STATA16 to identify common variable structures and define clusters. The factor analysis resulted in ninety-three common variables. To further cluster the keywords, we looked at term definition in prior research (e.g., Verhoef et al., 2019; Fabian et al., 2020). The final dictionary is presented in Appendix B and includes three clusters and three sub-clusters. Lastly, we assessed the quality of the CATA by performing a robustness check. To perform the robustness check, we randomly picked fifty companies from the dataset. Based on their current website and 10k filing (the year 2019), we ranked the companies from one, meaning not focussing on digital transformation, to fifty, meaning very much focus on digital transformation (Appendix C). After ranking the fifty companies, we compared our ranking with ranking based on the actual word count in the year 2019. After comparing, we found that the CATA gives a good indication of the degree of digital transformation, meaning that the CATA is robust.

3.2 Measures

To measure the relation between the different constructs, I constructed the data. This section explains the variables used in the analysis and the chosen measurements. Throughout this section, I elaborate on the statistical choices I have made.

3.2.1. Independent variable: digital transformation

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analysis on the CATA results, I created three clusters and three sub-clusters with which the digital transformation efforts are defined. The clusters include:

1. Digitisation: described as the action of converting analog information into digital information

2. Digitalisation: described as the process of applying digital strategies to optimize existing business processes

3. Digital transformation: described as the transformation of essential products and processes, organizational structures, and management concepts to develop sustainable competitive advantages.

a) Big data – transforming digitally by using assets such as big data analytics and deep learning to understand customers better and respond accordingly

b) Process optimization – transform digitally to optimize or create new business processes to reduce costs and improve time to the market.

c) Platform usage – transform digitally by using different digital channels, which allow for reaching new and bigger customer groups.

To measure digital transformation, I use the keywords (Appendix B) count in the 10k filing. Additionally, I control for the length of the 10k filings by looking at the total word count.

3.2.2. Dependent variable: firm financial performance

To measure financial performance, I look at two dimensions: operating performance and market performance. Operating performance allows evaluating the past and present organizational adaption and is measured by looking at total revenue, operating costs (Melville et al., 2004), and net income. An increase in revenue, net income, and a decrease in operating costs suggest a positive effect. By looking at market performance, I look at a firm’s ability to transform itself when confronted with anticipated and unanticipated environmental challenges taking a more long-term perspective (Keats and Hitt, 1988). Their response is measured by looking at Tobin’s Q, an often-used measure (e.g., Fabian et al., 2020). Both measures are interrelated (Keats and Hitt, 1988), and I, therefore, expect them to be correlated as well (Landsman & Shapiror, 1995).

3.2.3. Moderating variable: environmental dynamism

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were grouped into the industry sector categories as shown in Appendix D. To calculate the annual sales per sector, I summed the net sales of all firms in a specific sector per year, followed by a regression. I used annual sales as the dependent variable and year with a window of 5 years as the independent variable for the regression. I then divided the regression coefficient's standard error by the mean sales value of the 15 years. This measure shows the rate of environmental dynamism in each different industry sector.

3.2.4. Control variables

Firm Size. Firm size can influence firm activities and performance (Baum and Wally, 2003;

Josefy et al., 2015), as they may have better access to and assimilation possibilities of (financial) resources (Stock et al., 2004). Also, larger firms may better recognize opportunities as they may have specific departments or teams for such activities (Vahlne & Johnsson, 2017). Firm size is measured by looking at the number of employees as suggested by Rai et al. (2006).

Firm age. Furthermore, I control for firm age as firms often go through life cycles where

they reach a maturity stage at some point during which a firm become inertial and performance levels off (Hitt et al., 2020). I establish the firm age by subtracting the foundation year from 2020.

R&D Intensity. A study done by Bhattacharya & Bloch (2004) illustrates that R&D

intensity can further induce innovation activity. A high R&D intensity is not a sole indicator for innovation performance but can stimulate innovation by dedicating more resources than lower R&D intense firms (Bhattacharya & Bloch, 2004; Savrul & Incekara, 2015). Thus R&D intensity is assumed to hold influence over innovation performance and therefore included in my controls. I have divided research and development expenses over total assets for each firm and each year to control for R&D intensity.

CEO duality. CEO duality, meaning that a CEO is at the same time chairman of the

board, is indicated with a binary variable. As CEO duality impacts Tobin’s Q, I controlled for this (Alexander, 2018).

CEO total compensation. The CEO total compensation is measure by adding up CEO

equity existing of the value of stock and option awards and long-term incentives granted, CEO cash compensation, and other CEO compensation items.

Year-fixed effects. To account for any year fixed effects, I include a dummy variable

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Table 1 gives and overview of the measurement for all variables.

Variable Type Variable Name Measure Independent

Variable

Digital

transformation

The number of cluster 3-keywords used in the 10k filing (Appendix B).

Dependent

Variables Firm performance financial Revenue in $ = revenues/1000000 Net income in $ = net income/1000000

Operational costs in $ = operational costs/1000000 Tobin’s Q = (total liabilities + (stock price x shares)) / total assets

Moderators Environmental dynamism

Dynamism = standard error of industry net sales / average net sales

Firm

Characteristic Controls

Firm age Firm age = 2020 - foundation year

Firm size Firm size = Number of employees

CEO total

compensation CEO total compensation in $ = ceo cash + ceo equity + ceo other earnings

Leverage Leverage = total liability / total assets

CEO Duality 0 indicates non-duality and 1 indicates CEO duality

10K file length 10k file length = Total word count 10k filing

Fixed effect Year Control for year fixed effects by i.year for all 15 years in the dataset

Table 1: Variable measures

4. Data analysis

To run the CATA, I use Python 3.9.1 and Jupyter notebook. To analyze the developed hypotheses, I use the statistical software program STATA16. As the dataset contains observations for the years 2005 to 2019, I use a panel analysis.

First, the CATA results containing the measurement of the independent variable are merged with the dataset containing the measurement of the dependent and control variables. Second, the moderating variable is created and merged with the dataset containing the dependent, independent, and control variables. Following, the final sample is defined, which resulted in 8989 observations consisting of 1037 companies.

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analysis and ensure all variables contribute to a scale when added together, I used the independent and control variables' standardized values. Furthermore, to take care of outliers, I winsorized the dependent and independent variables at the 2.5 percentile in each tail. As I am only interested in analyzing the impact of digital transformation that varies over time and want to control for aspects within the individual firms that may impact or bias the dependent variable, I use the fixed-effect model. The fixed-effect model assumes that time-invariant characteristics are unique to the companies and should not be correlated with other company characteristics. The fixed-effect model removes these effects so that I can assess the net effect of digital transformation on the firm financial performance (Hausman, 1978). The results from the Hausman test (Hausman & Taylor, 1981) confirmed this approach. To exclude the presence of multicollinearity, I conducted a VIF test. The highest VIF value is 3.28, which is far below the suggested maximum of 10 by Hair et al. (1995). In OLS, it is assumed that the error term has a constant variance meaning that the variance should be homoscedastic. I, therefore, performed a Breusch-Pagan and Cook-Weisberg test for heteroskedasticity. The result rejected the null hypothesis of constant variance, meaning that the variance is heteroscedastic. To avoid problems during the OLS, I take the robust standard errors while performing the OLS.

5. Results

5.1 Descriptive statistics and correlations

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Table 2: Descriptive statistics

Variable Observations Mean Std. Dev. Min Max

Revenues 8989 971,13 2930,09 -7,14 52396,4

Income 8989 1977,95 45152,39 -12650 2047100

Operating costs 8989 10286,03 249776,4 -92500 2.19e+07

Tobin's Q 8989 1,96 1,35 0,76 5,98 Digital transformation 8989 8,41 11,68 0 44 Environmental Dynamism 8989 ,02 ,03 ,00 ,24 R&D intensity 8989 ,09 2,99 -.03 213,87 CEO duality 8989 ,54 ,49 0 1 CEO total

compensation 8989 9004294 7.72e+07 0 5.70e+09

Firm age 8989 63 44 5 237

10k file length 8989 61727,99 47778,34 0 2204757

Firm size

(employees) 8989 24444,53 88014,87 0 2300000

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17 N = 7788

*** p < .01, ** p <.05, *p <.10

Table 3: Correlations, means, S.D.

Variables 1 2 3 4 5 6 7 8 9 10 11 12 Dependent variables 1 Total revenues 2 Net income 0.76*** 3 Operational costs 0.79*** 0.66*** 4 Tobin's Q -0.09 -0.03 -0.08 Independent variable 5 Digital transformation 0.12 0.13 0.13 0.16 Moderating variable 6 Environmental dynamism -0.03 -0.03 -0.03 -0.11 -0.19*** Control variables 7 R&D intensity 0.00 0.01 0.00 0.06 -0.00 -0.01 8 CEO duality 0.13 0.13 0.12 -0.02 -0.08 0.05 0.01

9 CEO total compensation 0.06 0.07 0.05 -0.00 0.04 -0.01 0.00 -0.00

10 Firm age 0.22*** 0.20*** 0.19*** -0.18 -0.20*** 0.08 -0.00 0.13 0.01

11 Firm size 0.44*** 0.39*** 0.40*** -0.01 0.09 -0.05 -0.00 0.03 0.03 0.07

12 Leverage 0.03 0.01 0.01 0.04 0.02 -0.00 0.01 0.01 0.00 0.03 0.00

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5.2 Regression results and hypothesis testing

I conducted an Ordinary Least Squares (OLS) regression to test the proposed hypotheses. Table 4 presents the results. The number of observations is constant across all regression models (N=7788). It is slightly lower than the presented number of observations in the descriptive statistics (N=8989) because of the included lagged variables of the dependent variables. Behind each model number, the measure which is taken as the dependent variable is presented. Thus, for models 1 to 3, revenue is taken as the dependent variable, for model 4 to 6, income is taken as the dependent variable, etc. The BIC values show a small increase with model 3, 6, 9, and 12 having the highest values, which indicates a probability of very minimal information loss, which is a positive given. The F value for all models was 0.0000, meaning that H0 ‘all of the regression coefficients are equal to zero’ can be rejected for all models.

Furthermore, except for models 1 to 3, the R-squared did not change over the models. The squared increased by 1 percent from model 2 to 3, which is a positive given. The R-squared values range from 11 to 34, meaning that 11 to 34 percent of the variance is explained by all covariates, which is, again, a positive given. Because of the high number of models and variables, I only look at the results considering the independent, moderating, and dependent variables. Whether significant or not, the control variables are all relevant in this study as they control for omitted variable biases, meaning that they can influence either the independent or dependent variable. They are, therefore, included in the model.

To be able to interpret the marginal effects of environmental dynamism, I plot four figures presenting the marginal effect at different values of the dependent and independent variable (Figure 1, Figure 2, Figure 3, Figure 4). The dotted line indicates a zero-marginal effect, so neither positive nor negative. The vertical grey lines represent the 95% confidence interval for any given marginal effect.

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of digital transformation on revenue. Figure 1 is consistent with the moderating coefficient from model 3 (Table 4) insofar as the positive relationship between digital transformation and revenue decreases as environmental dynamism increases. I find that environmental dynamism weakens the positive effect of digital transformation on revenue but only for very low digital transformation values. This result is partially in line with the expectation that firms in dynamic environments that put effort into digital transformation can better respond to customer needs and thereby increase their revenue (H2a). Firms that put effort into digital transformation and are active in a dynamic environment are not significantly better at increasing revenue, but they are less influenced by the dynamic environment.

Income. Model 4, 5, and 6 represent the effect of the control variables, independent variable, and moderating variable on net income, respectively. Model 5 presents a significant association between digital transformation and income (b = 36.80, p=0.038), meaning that when a company puts more effort into digital transformation, income increases.

The interaction term in model 6 is not significant, supported by Figure 2, meaning that the positive moderating effect of environmental dynamism on digital transformation and net income cannot be supported (H2a).

Operational costs. Model 7, 8, and 9, and relating Figure 3 represent the effect of the control variables, independent variable, and moderating variable on operational costs, respectively. No significant relationship is found between digital transformation and operational costs, neither for environmental dynamism moderating this relationship.

Tobin’s Q. Model 10, 11, and 12 represent the effect of the control variables, independent variable, and moderating variable on Tobin’s Q, respectively. Model 12 presents a weak significant negative moderating effect on the relationship between digital transformation and Tobin’s Q (b= -2,66, p=0.051), meaning that when a firm operates in a dynamic environment, Tobin’s Q decreases. This is also visualized in Figure 4. However, in Figure 4, we see that this significant moderating effect is only valid up to the 75th percentile of digital transformation, after which the marginal effect decreases below zero. The reason for this might be that stock price decreases because it is harder to predict the future market performance.

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OLS Regression Results

VARIABLES Model 1

Revenue Model 2 Revenue Model 3 Revenue Model 4 Income Model 5 Income Model 6 Income Model 7 Operation al cost Model 8 Operation al cost Model 9 Operation al cost Model 10

Tobin’s Q Model 11 Tobin’s Q Model 12 Tobin’s Q Independent variable Digital transformation 14.52 28.23* 36.80* 47.23* 47.76 32.35 -0.02 0.01 (10.11) (11.39) (17.74) (20.87) (36.44) (45.21) (0.03) (0.03) Moderating variable -1,379.62*** -1,056.37 1,557.42 -2.66+ Digital Transformation x Dynamism (366.79) (925.71) (2,468.39) (1.36) Control variables R&D intensity 1.28 1.29 1.23 16.39 16.41 16.36 4.47 4.51 4.57 0.05*** 0.05*** 0.05*** (1.05) (1.07) (1.09) (12.61) (12.65) (12.67) (3.84) (3.91) (3.90) (0.01) (0.01) (0.01) CEO duality 2.33 2.20 2.02 12.58 12.27 12.14 10.69 10.26 10.46 0.01 0.01 0.00 (6.92) (6.89) (6.83) (12.87) (12.93) (12.91) (36.40) (36.40) (36.38) (0.02) (0.02) (0.02)

CEO total compensation 1.30 1.27 1.14 6.56* 6.48* 6.38* 29.53 29.40 29.56 0.00 0.00 0.00

(1.02) (0.91) (1.01) (2.89) (2.59) (2.67) (24.22) (23.78) (23.66) (0.00) (0.00) (0.00) Firm age -8.32 -9.09 -8.90 -55.95 -58.02 -57.92 116.80 114.16 113.99 -0.04 -0.04 -0.04 (17.87) (18.74) (16.59) (62.73) (64.56) (63.39) (73.76) (72.71) (72.36) (0.06) (0.06) (0.05) 10k file length 5.15 4.40 4.54 -3.84 -5.77 -5.67 44.30 41.85 41.69 0.00 0.00 0.00 (4.29) (3.86) (3.98) (6.55) (7.03) (6.97) (30.11) (28.90) (28.76) (0.01) (0.01) (0.01) Firm size 135.24* 134.01* 131.16* 244.21* 240.15* 237.56* 1,006.14** 1,001.45** 1,005.04** -0.09 -0.09 -0.09 (59.86) (59.77) (59.51) (105.73) (104.91) (103.94) (341.19) (340.99) (341.54) (0.12) (0.12) (0.12) Leverage 3.28* 3.14* 3.09* -3.25*** -3.67*** -3.74*** 23.16*** 22.67*** 22.75*** 0.06*** 0.06*** 0.06*** (1.38) (1.36) (1.36) (0.29) (0.37) (0.39) (4.84) (4.85) (4.87) (0.00) (0.00) (0.00)

Year-fixed effects yes yes yes yes yes yes yes yes yes yes yes yes

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22 Tobin’s Q = L, 0.39*** 0.39*** 0.39*** (0.03) (0.03) (0.03) R-squared 0.29 0.30 0.30 0.11 0.11 0.11 0.30 0.30 0.30 0.19 0.19 0.19 Number of firms 1,037 1,037 1,037 1,037 1,037 1,037 1,037 1,037 1,037 1,037 1,037 1,037 AIC 103391 103388 103368 116044 116038 116038 130188 130188 130189 15091 15092 15087 BIC 103503 103506 103493 116155 116157 116163 130299 130306 130314 15203 15211 15213 Log-likelihood -51680 -51677 -51666 -58006 -58002 -58001 -65078 -65077 -65077 -7530 -7529 -7526

Robust standard errors in parentheses *** p<0.001, ** p<0.01, * p<0.05, + p<0.1 N = 7788

Table 4: OLS regression result Marginal effect models

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Figure 3: Marginal effects of environmental dynamism on operational costs Figure 4: Marginal effects of environmental dynamism on

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6. Discussion

6.1 Theoretical contributions and implications

This study aimed to investigate the financial performance effects of digital transformation. To test the hypothesis, I created a unique dataset consisting of 1037 American companies.

First, in my literature review, I found that that digital transformation can lead to revenue and income increase (Cheah & Wang, 2017; Rachinger et al., 2018) as well as cost reduction (Garicano & Kaplan, 2001), and thus has a positive effect on firm financial performance in general. I sought to clarify this conceptual argument by looking at different financial measures. This was done incorporating revenues, income, operational costs, and Tobin’s Q as financial measures. This study's empirical findings only partially confirm the proposed hypothesis that digital transformation leads to overall increased firm financial performance (H1). The results indicate a significant positive effect on net income but do not show a significant effect on revenue, cost reduction, or increased market performance. A potential explanation for the insignificant results is that transforming digitally also involves challenges such as the ability to integrate ‘digital’ into the DNA of the business models (Lucas & Goh, 2013; Schallmo et al., 2017; Reis et al., 2018), or, more radical, the creation of new business models. The involved learning costs and investments made can result in lower or adverse performance effects in the short term (Nelson & Winter, 2002). A significant increase in net income but not in revenue might indicate that digital transformation enables firms to increase customer engagement and participation (Vial, 2019) and thereby to sell the same amount of goods and services at lower costs of selling.

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transformation efforts in a dynamic environment does not strengthen the cost-reducing effect of digital transformation.

The empirical findings in this study do not confirm the proposed hypothesis that a firm digitally transforming while faced with environmental change, uncertainty, and risk, is better able to increase revenue, net income, or market performance due to digital transformation. Hypothesis 2a is not supported. Contrary, I found that a dynamic environment weakens the positive effect of digital transformation on revenue and market performance at low digital transformation levels. The proposed hypothesis that the focus shifts away from cost reduction when faced with environmental change, uncertainty, and risk (H2b), is also not supported.

A possible explanation for the insignificant interaction effect of environmental dynamism might be that a manager must first recognize a ‘productive opportunity’ as such before he or she decided to apply resources to that opportunity (Kor, Mahoney, and Michael, 2007). That means that the environment’s objective state does not present opportunities by itself (Foss, 1998). Furthermore, as my dataset contains large organizations (Mean number of employees = 24437), the attention within multi-divisional organizations may not always be uniform. The relevance of environmental situations might vary according to the decision-makers structural position (Gaba & Joseph, 2013). Members might have an imperfect and divergent understanding of environmental signals recognized in attention-based perspectives (Weick and Sutcliffe, 2006; Rerup, 2009). Another explanation for this might be that digital transformation assets are not mature enough to be easily applied in uncertain and risky situations.

This study contributes to digital transformation in a twofold manner. Firstly, it contributes to digital transformation research by providing quantitative measures for digital transformation's financial performance effects. Knowing whether digital transformation efforts mainly result in cost reduction or revenue increase over time enables managers to make better-informed decisions while transforming digitally. Second, I contribute to the literature on the attention-based view by explaining how environmental dynamism influences managerial actions regarding digital transformation. Understanding whether environmental dynamism strengthens or weakens digital transformation efforts can help managers make better-informed decisions when faced with uncertainty and risk.

6.2 Managerial implications

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to consider their structural position within the company and act cross-functionally when making strategic decisions about digital transformation when faced with environmental dynamism.

6.3 Limitations and future research

Several limitations are noteworthy. Firstly, there might have been selection bias regarding the data collection process by only selecting the top-performing American companies. Within this study, I can only speak about rather large, well-performing companies, incumbent firms. Additionally, while the dataset consists of over 20000 observations, the final sample only included 8989 observations. Following, a lot of year-specific data got lost, which may have influenced over-time results. Also, after adding 189 additional words to the dictionary based on the KWIC analysis, a new KWIC might have been appropriate to assess the 189 added words. Due to time limits, this has not been done. Secondly, limitations regarding the data analysis are that the CATA, aimed at assessing digital transformation, was only applied to 10K filings. This analysis might have yielded different results when applied to the annual reports or about us sections. Also, as a robustness check, performing additional analysis for the cluster 1, 2, and subclusters 3a, 3b, and 3c (Appendix A) independently might have yielded better insights to explain why the results are as they are. Thirdly, there is no literature about managerial attention to digital transformation, making it challenging to interpret strategic decisions toward digital transformation based on attention perspectives.

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Appendix

Appendix A

Main cluster Sub cluster Cluster assets Financial interest 1. Digitisation 2. Digitalisation Reduce coordination-, transaction, agent- costs 3. Digital Transformation 3a. Big data Machine learning Big data analytics Machine learning Deep learning Algorithms Reduce costs of selling; increase price margins by increasing customer satisfaction 3b. Process

optimisation RFID Internet of Things Artificial

intelligence Blockchain Robotics Virtual agents

Reduce supply chain costs; reduce HR costs; reduce coordination-, transaction, agent- costs 3c. Platform

usage Digital communication Virtual networks Web-based sales Augmented reality Virtual reality

Revenue growth by reaching more and

new customers;

increase equity per employee

Table 5: Overview of clusters and the respective financial interest

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Table 6: Dictionary

Main cluster Sub cluster Cluster assets Keywords

1. Digitisation Digital, digitally, digitally enabled, digitisation, digitization, ICT, internet, internet-based, software, computer network, computer systems,

2. Digitalisation digitalisation, digitalization, ecom, ecommerce, multi-channel, multichannel, online, online-marketing, webshop, website, e-commerce,

internet sites, internet websites, IT environment, IT infrastructure, IT professionals, IT support, IT systems, web page, web presence, web site, digital advertising, digital assets, digital capabilities, digital problems, digital products, digital sales, digital strategy, digital systems, digital technologies, digital technology, electronic commerce, new technologies, new technologies, new technology, IaaS, license software, licensed software, information and communication technology, information technology systems, internet advertising, internet security, electronic marketplace, email marketing, emerging technologies, emerging technology, online stores, online strategy, online survey, online tools, online training, online transaction, online users, online offering, online ordering, online ordering system, online orders, online payment, online payment system, online portal, online presence, online reputation management, online resources, online retailer, online sales, online service, online services, online shop, online shopping, online marketplace, online booking, online business, online channels, online consumer, online account, online activities, online banking interface, online storage, online verification 3. Digital Transformation

a. Big data Machine learning Big data analytics Machine learning Deep learning Algorithms

cloud, cloud-based, cloud-capable, cloud application, cloud computing, cloud environments, cloud infrastructure, cloud platform, cloud products, cloud security, cloud service, cloud services, cloud solutions, algorithm, algorithms, algorithmic, data, data-driven, database, databases, datacenter, data centers, data cloud, data clouds, data communication, data mining, data processing services, data scientists, data warehousing, deep learning, deep neural networks, metadata, pay-per-click, pay-per-view, multi-cloud, text-mining, 3D analysis, advanced analytics, big data, big data researchers, business intelligence, digital data, digital transformation, digital transformation project, machine learning, image processing, targeted advertising, transaction data, predictive modelling,

cognitive systems, computer vision, computer vision algorithms, computer vision intelligence, computer vision solution, computer vision technology, computerized simulation, customer portal, cyber security control, motion detection, multi-structured data, electronic data interchange, metasearch engines, online behavior, behaviour, sales cloud, service cloud, smart cities, smart home, smart meters b. Process optimisation RFID Internet of Things Artificial intelligence Blockchain Robotics Virtual agents

AI, bitcoin, blockchain, cryptocurrency, RFID, robotics, IoT, s/4, LaaS, cyber-security, cybersecurity, ai-based applications, 3D printing, additive manufacturing, AI data scientist, artificial intelligence, as-as-service infrastructure, chief digital officer, chief technology officer, digital leader, digital solutions, natural interfaces, network infrastructure, neural networks, high tech, high-speed wireless, home automation devices, radio-frequency identification, rapid prototyping, face recognition, facial recognition, factory automation, voice assistant software, three-dimensional printing, training software, people recognition, person detection, internet of things, ERP system, carrier portal, master tag, supply chain analytics, software engineers, software as a service, SAP system, search engine optimization, sensor fusion, software developers, software license arrangements, software licenses,

c. Platform

usage Digital communication Virtual networks Augmented reality Virtual reality Applications

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Appendix C Firm_name Level of digital transformation based on company website and 10k filing Company place (our guess) 1-50 Relative number of words used (after performing CATA)

Company place (based on relative words used) 1-50

Aerie Pharmaceuticals Inc website 1 0.01794 8

Assured Guaranty Ltd. website 2 0.00999 4

Ames National Corporation website 3 0.01777 7

Alliant Energy Corporation website 4 0.01269 6

Delek US Holdings Inc. online 5 0.01048 5

CubeSmart website, self-service 6 0.00767 2

Altria Group Inc. website, online shop 7 0.00521 1

Legg Mason Inc. website 8 0.02118 9

Arcadia Biosciences Inc website 9 0.03379 19

Clean Harbors Inc. website 10 0.04444 24

IPG Photonics Corporation website 11 0.06838 29

Dave & Buster's Entertainment,

Inc. website 12 0.08091 32

AAON Inc. website 13 0.22468 42

Micron Technology Inc. website, IoT 14 0.07137 30

CytRX Corporation website, online 15 0.05293 25

Westar Energy website 16 0.00882 3

Balchem Corporation website 17 0.02493 13

Noble Energy, Inc.

website, digital advertising, big data

analytics 18 0.03198 18

Hartford Financial Services Group

Inc. website, digital technology 19 0.02267 11

Altisource Portfolio Solutions S.A. website, mobile app 20 0.03029 16

Daktronics Inc. digital marketing 21 0.08899 33

CVB Financial Corp. website, cybersecurity, online 22 0.03456 20

Ecolab Inc. website, digital solutions, digital dashboard, smart solutions, automation, data analytics 23 0.02217 10

First Horizon National Corp.

website, digital, data

centric, mobile banking 24 0.02647 14

Crawford & Co. technology, on-demand, digital assets, 3d 25 0.02968 15

Service Corporation International

website, digitally, digital presentation, digital presence, personalized, customized, search engine optimization, online marketing, data analytics, lead tracking, digital platform, mobile,

Master’s in Economics and Business