Profiting from Artificial Intelligence: Data as a source of competitive advantage

By Philipp Max Hartmann

While Artificial intelligence is considered to be the engine of innovation and growth for years to come, little is known about the factors that secure a competitive advantage for companies using it. This thesis addresses this gap. Combining case study research and survey research, this study provides empirical evidence for the resource data as a potential source of competitive advantage but contingent to the type of offering. The study further propose data as a complementary asset that partially explains a strong increase of corporate research in the field of artificial intelligence contradictory to an overall decline of corporate science activities.

• Language: English
• Publisher: Books on Demand
• Release date: Jul 9, 2020
• ISBN: 9783751966405

Philipp Max Hartmann

Philipp Hartmann advises companies on how to stay competitive in the age of AI. Currently he is Director of AI Strategy at appliedAI, before that he spent four years at McKinsey & Company as a strategy consultant. Philipp holds a PhD from Technical University of Munich where he investigated factors of competitive advantage in Artificial Intelligence. He studied Industrial Engineering at the Karlsruhe Institute of Technology and the University of Cambridge.

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TECHNISCHE UNIVERSITÄT MÜNCHEN

Fakultät für Wirtschaftswissenschaften

Dr. Theo Schöller-Stiftungslehrstuhl für Technologie- und

Innovationsmanagement

Vollständiger Abdruck der von der Fakultät für Wirtschaftswissenschaften der Technischen Universität München zur Erlangung des akademischen Grades eines

Doktors der Wirtschaftswissenschaften (Dr. rer. pol.)

genehmigten Dissertation.

Die Dissertation wurde am 24.07.2019 bei der Technischen Universität München eingereicht und durch die Fakultät für Wirtschaftswissenschaften am 15.10.2019 angenommen.

Acknowledgments

Writing this dissertation would not have been possible without the support of many people, to whom I would like to express my deep gratitude.

First and foremost, I would like to thank my supervisor Prof. Joachim Henkel for supporting, advising, and challenging me during this dissertation project. With his deep passion for research, wealth of experience, and integrity, he is the doctoral supervisor every student hopes to have. Furthermore, I would like to thank Prof. Christoph Ungemach for acting as a second supervisor of my dissertation, as well as Prof. Alwine Mohnen for chairing my dissertation committee.

Presenting my work at several seminars - in particular the TIME seminar - and conferences gave me the opportunity to receive valuable feedback; thanks to all official and unofficial discussants for their comments that further shaped this dissertation.

I had the great opportunity to spend a few months at the University of Cambridge and would like to thank Prof. Andy Neely and Dr. Mohamed Zaki for inviting and hosting me during this time.

Many industry experts contributed to this thesis by dedicating time for interviews - their willingness to openly share their experiences laid the foundation for this research. Furthermore, I am grateful to all of the participants of my survey.

Writing this dissertation would have been twice as hard and half as much fun without the wonderful colleagues at the TIM chair that always helped with any academic and non-academic issues. Furthermore, I had the pleasure to work with some very talented student assistants that supported my research and carried out even tedious tasks with great dedication.

Finally, I would like to thank my family for their continuous support and encouragement. In particular, I would like to thank my wife Nikola and my son Leopold - you are my sunshine!

Table of contents

List of figures

List of tables

List of abbreviations

Abstract

Introduction

1.1 Motivation

1.2 Research objectives and research design

1.3 Structure of the thesis

Theoretical foundations

2.1 Introduction

2.2 Toward a definition of data-driven innovation

2.2.1 Overview

2.2.2 Existing definitions of data-driven innovation

2.2.3 What does data-driven mean?

2.2.4 What is an innovation?

2.2.5 Definition of data-driven innovation

2.3 Technical foundations: Artificial intelligence and machine learning

2.3.1 Artificial intelligence

2.3.2 Machine learning

2.3.3 Deep learning

2.3.4 Machine learning-based programs

2.4 Profiting from data-driven innovation

2.4.1 Overview

2.4.2 Data-driven process innovation

2.4.3 Data-driven product or service innovation

2.4.4 Data-driven business model innovation

2.4.5 Data as a barrier to competition

2.5 Conclusion

A resource-based perspective on data-driven innovation

3.1 Introduction

3.2 The resource-based view

3.2.1 Propositions of the resource-based view

3.2.2 Critique of the resource-based view

3.2.3 Resource complementarity

3.3 Resource-based view of IT and data-driven innovation

3.3.1 Overview

3.3.2 Resource-based view in information systems research

3.3.3 Resource-based research on data-driven innovation

3.4 Resources for data-driven innovation

3.4.1 Overview

3.4.2 Data

3.4.3 Algorithm

3.4.4 IT infrastructure

3.4.5 Technical knowledge

3.4.6 Domain knowledge

3.4.7 Organizational assets

3.5 Summary and conclusion

How companies create value from data-driven innovation - qualitative research

4.1 Introduction

4.2 Methodology

4.2.1 Research Design

4.2.2 Sample selection

4.2.3 Data collection

4.2.4 Data analysis

4.2.5 Validity and reliability of research

4.3 Results

4.3.1 Types of data-driven innovations

4.3.2 Resources for data innovation

4.3.3 Factors hindering success

4.4 Discussion of qualitative results

4.4.1 Resources for data-driven innovation

4.4.2 Relation to existing research

4.4.3 Limitations

4.5 Summary and Conclusion

Securing a competitive advantage in data-driven innovation - quantitative study

5.1 Introduction

5.2 Methodology

5.2.1 Sample and sample selection

5.2.2 Data collection

5.3 Data and variables

5.3.1 Research design

5.3.2 Dependent variable

5.3.3 Independent variables: VRIN Scores

5.3.4 Control variables

5.3.5 Robustness of the research design

5.4 Descriptive results

5.4.1 Sample characteristics

5.4.2 Resource importance

5.4.3 VRIN characteristics

5.4.4 Sources of resources

5.5 Regression analysis

5.5.1 Results

5.5.2 Regression diagnostics

5.6 Discussion

5.7 Conclusion and limitations

The rise of corporate research in AI

6.1 Introduction

6.2 The rise and decline of corporate science

6.2.1 What is corporate science?

6.2.2 Motivation for corporate science

6.2.3 Motivation for revealing the results

6.2.4 The decline of corporate science

6.3 Data and data collection

6.4 Results

6.4.1 Share of corporate publications

6.4.2 Quality of corporate science paper

6.4.3 Major publishers of corporate research in AI

6.4.4 Affiliation of top researchers

6.4.5 Patents of major tech companies

6.4.6 Acquisition of AI companies

6.4.7 Key financial data

6.5 Discussion

6.6 Conclusion, implications, and limitations

Conclusion

7.1 Key results and research implications

7.2 Managerial implications

7.3 Policy implications

Appendix

A 1 Interview questionnaire

A 2 Qualitative research (Chapter 4) coding tree

A 3 Survey (Chapter 5) sample demographics

A 4 Survey (Chapter 5) descriptive results

References

List of figures

Figure 1: Structure of thesis

Figure 2: Google search trend for related concepts

Figure 3: Basic principle of deep learning (adapted from LeCun et al., 2015)

Figure 4: Differences between traditional programs and machine learning/Deep learning-based programs (adapted from Goodfellow et al., 2016, p. 10)

Figure 5: Stated goals of using artificial intelligence (Davenport and Ronanki, 2018)..

Figure 6: Categories of data-driven innovation

Figure 7: Overview of resource conceptualizations in the context of big data analytics

Figure 8: Resources for data-driven innovation

Figure 9: Data analysis process

Figure 10: Overview of applications of DDI, types of innovations and maturity

Figure 11: Factors hindering data-driven innovation

Figure 12: Resources for data-driven innovation

Figure 13: Summary of survey design process (based on Groves, 2009)

Figure 14: Summary of sampling process

Figure 15: Overview of research design

Figure 16: Calculation of additive and multiplicative VRIN scores

Figure 17: Characteristics of sample

Figure 18: Importance of resources

Figure 19: Additive VRIN Scores for resources

Figure 20: Multiplicative VRIN Scores for resources

Figure 21: Correlation among VRIN Scores

Figure 22: Fitted values plotted against standardized residuals

Figure 23: Standardized residuals plotted against standard normal distribution

Figure 24: Cook's Distance Di for all observations i

Figure 25: Average indexed share of corporate publications for both groups of conferences

Figure 26: LOESS fitted line of average indexed corporate share

Figure 27: Indexed relative citations for AI conferences

Figure 28: Number of yearly filed patents in AI (patent families)

List of tables

Table 1: Overview of IS resources

Table 2 Overview of key AI advances and the respective algorithms and datasets (Wissner-Gross, 2016)

Table 3: Overview of case firms

Table 4: List of qualitative interviews

Table 5: Overview of control variables

Table 6: VRIN characteristics of resources

Table 7: Sources of algorithms and data used by companies in the sample

Table 8: Ordinary least squares (OLS) Regressions

Table 9 Conference sample for AI as well as control group

Table 10: Absolute number (abs.) and relative share (rel.) of corporate publications

Table 11: Most active companies contributing to the AI conferences in the samples

Table 12: Former and current affiliation of top researchers (deep learning)

Table 13: Companies employing the leading researchers

Table 14: Number of Al-related acquisitions

List of abbreviations

Abstract

Artificial intelligence and machine learning—its most important subfield — are considered the engines of innovation and growth for years to come and data is considered the fuel. Companies across a range of industries are already using data and machine learning to improve existing processes, create new products or entirely new business models to solve problems for everything from fraud detection to crop predictions. However, so far, little is known about the factors needed to secure a competitive advantage for companies that use machine learning. This thesis addresses this gap. Overall, the results suggest it is the resource data that secures a competitive advantage in machine learning while the technology is relatively less important. However, this is contingent to the specific application. This thesis makes three key contributions.

First, based on a mixed-methods study, combining qualitative and quantitative research, I provide empirical evidence for the resource data as a potential source of competitive advantage but contingent to the respective offering. A set of different resources is required for data-driven innovation. But not all of these resources are equally important in securing a competitive advantage. The qualitative study shows it is primarily data that firms consider key for securing a competitive advantage. The results of the quantitative study confirm this. Start-up companies with data as a strategic asset are associated with more VC funding and regarded a proxy for future success. However, this relationship depends on the type of offering.

Second, I propose complementary assets as a novel explanation for companies to invest in basic research. I propose it is the availability of data as a complementary asset that (partially) explains the strong increase of corporate research in artificial intelligence contradictory to an overall decline of corporate science activities. I document this increase using corporate publications at the most important AI and machine learning conferences. By controlling large data assets, the companies that invest in corporate AI research benefit strongly from scientific advances, have an advantage in researching AI, and can appropriate a large share of the value of their research.

Third, by providing a definition of data-driven innovation and a set of resources needed, I lay the foundation for subsequent research. Research on data-driven innovation is nascent, and several related concepts are used to describe a similar phenomenon. The proposed definition of data-driven innovation unites the different streams of literature. I further propose a set of resources for data-driven innovation derived from literature and empirically tested through case studies and quantitative survey data that might guide future resource-based research on data-driven innovation.

1 Introduction

1.1 Motivation

Artificial intelligence (AI) and machine learning—its most important subfield¹— are considered the engines of innovation and growth for years to come and data is considered the fuel (Brynjolfsson and McAfee, 2017b; Henke et al., 2016; OECD, 2015). Companies across a range of industries are already using data and machine learning to improve existing products and processes and to create new ones while start-up companies are creating entirely new business models to solve problems for everything from fraud detection to crop predictions (Gann et al., 2014). However, so far little is known about the factors securing a competitive advantage for companies that use machine learning (George et al., 2014).

Artificial intelligence is an umbrella term that is concerned with building intelligent entities (Russell and Norvig, 2009, p. 1) and encompasses various subfields including machine learning and robotics. The term 'artificial intelligence' was first coined in the 1950s and high expectations regarding potential applications had already been raised at that time. However, these high expectations were mostly unfulfilled as a lack of computing power as well as available datasets limited the practical applicability of the technology. In the last years, the processing power of computers has improved while costs for data storage and processing has decreased. At the same time the amount of data generated grew rapidly.

Subsequently AI and in particular machine learning applications have already achieved an impressive performance for specific tasks even surpassing human experts for tasks that would have been considered difficult for machines a few years ago. Most prominently, in 2016 Google's AlphaGo, a computer program for the board game GO, defeated grandmaster Lee Sedol, the eight-time GO world champion. In image and speech recognition machine learning algorithms are at par with humans (He et al., 2015). For example in healthcare, machine learning-based systems are better than human experts at detecting breast cancer metastasis² or skin cancer³ (Esteva et al., 2017; Liu et al., 2017). And machine learning is already behind many products that are used daily by millions of people. Virtual personal assistants such as Amazon's Alexa or Google Assistant are able to understand voice-based commands and provide automated answers. Transportation company Uber uses machine learning to provide precise estimated arrival times (Hermann and Del Balso, 2017). Entertainment company Netflix uses data and analytics to provide its customers with new movie recommendations (Amatriain and Basilico, 2012). Google uses machine learning to reduce the energy consumption of its data centers by up to 40 percent (Knight, 2018).

There is an abundance of articles from business press and white papers