lean startup as a tool for digital business model

Lean Startup as a Tool for Digital

Business Model Innovation: Enablers and

Barriers for Established Companies

MASTER THESIS

THESIS WITHIN: Business Administration

NUMBER OF CREDITS: 30 credits PROGRAMME OF STUDY: Digital Business AUTHOR: Maja Beisheim and Charline Langner JÖNKÖPING May 2021

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Master Thesis in Business Administration

Title: Lean Startup as a Tool for Digital Business Model Innovation: Enablers and

Barriers for Established Companies

Authors: Maja Beisheim and Charline Langner

Tutor: Henry Nelson Lopez Vega

Date: 2021-05-24

Key terms: Lean Startup, Digital Business Model Innovation, Business Models, Business

Experimentation, Established Companies, Agile Methods.

Abstract

Background: The rapidly changing world of digital technologies forces many companies to

undertake a digital shift by transforming existing business models into digital business models

to achieve sustainable value creation and value capture. Especially, for established companies,

that have been successful leaders before the dot-com bubble (1995-2000) and whose business

models have been threatened by the emergence of digital technologies, there is a need for a

digital shift. We refer to this digitization of business models as digital business model

innovation. However, often adoption and implementation of digital technologies require

tremendous changes and thus, can be challenging for established companies. Therefore, agile

methods and business experimentation have become important strategic elements and are used

to generate and test novel business models in a fast manner. We introduce lean startup as an

agile method for digital business model innovation, which has proven to be successful in digital

entrepreneurship. Thus, it requires further empirical investigation on how to use lean startup in

established companies for successful digital business model innovation.

Purpose: The purpose of our study is to identify enablers and barriers of lean startup as a tool

for digital BMI in established companies. Thus, we propose a framework showing how

established companies can be successful in digital business model innovation by using lean

startup.

Method: We conducted an exploratory, qualitative research based on grounded theory

following an abductive approach. Using a non-probability, purposive sampling strategy, we

gathered our empirical data through ten semi-structured interviews with experts in lean startup

and digital business model innovation, working in or with established companies, shifting their

business model towards a digital business model. By using grounded analysis, we gained an in-

depth understanding of how lean startup is used in practice as well as occurring barriers and

enablers for established companies.

Conclusion: We emphasize that successful use of lean startup for digital business model

innovation is based on an effective (1) lean startup management, appropriate (2) organizational

structures, fitting (3) culture, and dedicated (4) corporate governance, which all require and are

based on solid (5) methodical competence of the entire organization. Furthermore, (6) external

influences such as market conditions, role of competition, or governance rules indirectly affect

using lean startup as a tool for digital business model innovation.

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Table of content

List of Figures ................................................................................................................. iv

List of Tables ................................................................................................................... iv

List of Abbreviations ...................................................................................................... iv

1. Introduction ................................................................................................................. 1

1.1 Background ................................................................................................................. 1

1.2 Problem discussion ...................................................................................................... 3

1.3 Research question and research purpose ..................................................................... 3

1.4 Outline of the paper ..................................................................................................... 4

2. Literature Review ........................................................................................................ 5

2.1 Business Model Innovation ......................................................................................... 5

2.1.1 Business Models ........................................................................................ 5

2.1.2 The digital shift – Business Models and Digital Technologies ................. 6

2.1.3 Digital Business Model Innovation ............................................................ 8

2.2 Lean Startup as a Tool for Digital Business Model Innovation .................................. 9

2.2.1 Business Experimentation and Lean Startup............................................ 10

2.2.2 Lean Startup ............................................................................................. 10

2.2.3 Digital Business Model Innovation with Lean Startup in Established

Companies ............................................................................................... 15

3. Methodology .............................................................................................................. 17

3.1 Research Philosophy ................................................................................................. 17

3.2 Research Approach ................................................................................................... 18

3.3 Research Design ........................................................................................................ 19

3.3.1 Data Collection......................................................................................... 19

3.3.2 Sampling Strategy .................................................................................... 21

3.3.3 Interview Design ...................................................................................... 22

3.4 Data Analysis ............................................................................................................ 24

3.5 Research Ethics and Quality Insurance ..................................................................... 27

4. Empirical Findings .................................................................................................... 31

4.1 Lean Startup Management ........................................................................................ 34

4.1.1 Agility ...................................................................................................... 34

4.1.2 Digital Tools ............................................................................................ 35

4.1.3 Handling of Hypotheses ........................................................................... 35

4.1.4 Handling of Minimum Viable Product .................................................... 36

4.1.5 Customer Involvement ............................................................................. 37

4.2 Organizational Structures .......................................................................................... 38

4.3 Culture ....................................................................................................................... 40

4.3.1 Corporate Culture ..................................................................................... 40

4.3.2 Mindset ..................................................................................................... 41

4.3.3 Personal Culture ....................................................................................... 42

4.3.4 Motivation ................................................................................................ 42

4.4 Corporate Governance ............................................................................................... 43

4.4.1 Management ............................................................................................. 43

4.4.2 Leadership ................................................................................................ 44

4.5 Methodical Competence ............................................................................................ 46

4.5.1 Knowledge ............................................................................................... 46

4.5.2 Training and Education ............................................................................ 46

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4.5.3 Teaming.................................................................................................... 47

4.6 External Influences .................................................................................................... 48

5. Analysis ...................................................................................................................... 50

5.1 Framework of Enablers and Barriers for Lean Startup ............................................. 50

5.2 Digital Business Model Innovation with Lean Startup in Established Companies ... 52

5.2.1 External Influences................................................................................... 52

5.2.2 Methodical competence ........................................................................... 53

5.2.3 Organizational Structures, Culture, and Corporate Governance .............. 54

5.2.4 Lean Startup Management ....................................................................... 56

6. Conclusion & Discussion .......................................................................................... 58

6.1 Summary ................................................................................................................... 58

6.2 Theoretical Implications ............................................................................................ 59

6.3 Managerial Implications ............................................................................................ 60

6.4 Limitations and Future Research ............................................................................... 62

Reference list .................................................................................................................. 64

Appendices ...................................................................................................................... v

iv

List of Figures

Figure 1: Lean startup hypothesis process (Eisenmann et al., 2012). ............................. 12

Figure 2: Build-Measure-Learn loop (Ries, 2011). ......................................................... 13

Figure 3: Final coding tree with example codes. ............................................................ 26

Figure 4: Enablers and barriers for using lean startup as a tool for digital BMI. ............ 50

List of Tables

Table 1: Overview of Interview Partners. ....................................................................... 20

Table 2: Overview of categories and sub-categories with example codes and quotes. .. 33

List of Abbreviations

BM Business Model

BMI Business Model Innovation

MVP Minimal Viable Product

Jönköping International Business School, 2021 Maja Beisheim

Charline Langner

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1. Introduction

___________________________________________________________________________

This thesis examines lean startup as a tool for digital business model innovation in established

companies. The purpose of this part is to familiarize the reader with the research field by

introducing and bridging the main topics of business models and business model innovation

with digital technologies and lean startup. Further, it discusses the problem statement and

identifies research gaps, leading to the purpose and research question of this study. _______________________________________________________________________________________________________________________________________________________

1.1 Background

Every company explicitly or implicitly operates with a business model (BM), predicting what

customers want and need (Teece, 2010), describing how companies create and deliver value,

identifying customers and how companies engage with them (Amit & Zott, 2012a; Baden-

Fuller & Haefliger, 2013; Teece, 2010). To achieve sustainable value creation and value capture

as well as customer satisfaction, companies are required to continuously question and rethink

their BM to stay competitive (Achtenhagen et al., 2013; Amit & Zott, 2012b). Especially, digital

technologies have revolutionized how industries operate and how value is created and captured

in businesses (Dal Mas et al., 2020). This led to a transformation of business structures and

activities and requires companies to make organizational-wide changes (Brynjolfsson & Hitt,

2000).

The rapidly changing world of digital technologies empowers the digitization of products and

processes and forces many companies to follow that digital shift to keep up with the competition

and to fulfill customer and market needs (Chanias et al., 2019). Over the past years, new

technologies changed the nature of products and services, customer relationship management,

organizational structures, business operations, and even whole BMs (Bharadwaj et al., 2013;

Chanias et al., 2019; Correani et al., 2020; Rachinger et al., 2019; Westerman & Bonnet, 2015).

Thus, there is a need for companies to reinvent themselves to successfully adopt digital

technologies. However, besides opportunities such as increased flexibility, reactivity, and

product individualization, the digital shift also presents challenges such as the complexity of

using and implementing technology or changing customer behavior and preferences, as well as

legal requirements (Rachinger et al., 2019). For this study, we focus on a digital shift in the

context of business model innovation (BMI), caused by the adaptation and implementation of

digital technologies. Often, existing BMs are successful for a certain time period but need to be

adapted to new market conditions, forced by digital technologies, and if not, they risk failing


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(Achtenhagen et al., 2013). Therefore, BMI can be a powerful tool as it provides potential for

new products and services, and new markets (Amit & Zott, 2012b). In this context, digital

technologies are a facilitator of BMI which can either force BMs to change completely, by

replacing old ones, or they can advance value creation and delivery by extending existing BMs

(Baden-Fuller & Haefliger, 2013). Thus, adapting digital technologies shifts existing BMs

towards digital BMs. Therefore, we define digitization of BMs as digital BMI.

Especially established companies whose BMs have been threatened by the emergence of digital

technologies (Chanias et al., 2019), require a digital shift (Ross et al., 2016; Sebastian et al.,

2017). These companies have often been established leaders before the dotcom bubble (1995-

2000) and thus, experienced rather low competition and steady success. Mainly, these

established companies belong to traditional industries, such as finance, retail or automotive.

Often, their original BM does not include digital components and hence, is not prepared for

today’s competition (Ross et al., 2016). A good example of an established company, shifting

its BMs by adapting digital technologies, is Philips Lightning, as it moved from selling light

bulbs, electricity, and installations to selling lights as a service on a pay-per-use basis (Tekic &

Koroteev, 2019). In comparison to so-called born-digital companies which started in the digital

era such as Google, Amazon, or Facebook, established companies need to make tremendous

changes when adopting and implementing digital technologies (Ross et al., 2016). In most

cases, their processes, organizational structures, or BMs are not designed for adapting novel

digital technologies (Chanias et al., 2019). The challenge for these established companies is to

reinvent their BMs and to adopt digital technologies as well as to make strategic changes, while

simultaneously taking advantage of their well-established business and maintaining their

customer relationships (Sebastian et al., 2017). Many traditional companies are too slow and

cautious in transforming processes (Westerman & Bonnet, 2015). Also, established companies

often perceive digitization as a challenge rather than an opportunity for change.

Therefore, agile methods and business experimentation have become important strategic

elements. They are used to generate and test novel BMs in a fast manner (Bocken & Snihur,

2020). One of these agile methods within the context of business experimentation is lean startup

(Ries, 2011). This method is a customer-centric approach aiming to identify the preferences

and needs of customers through feedback loops and step-by-step adjustments of products and

services. Lean startup has mainly been investigated for BMI within digital entrepreneurship


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(Ghezzi, 2020; Ghezzi & Cavallo, 2020). Also, it has proven to be an agile, innovative, and

successful method to facilitate change processes (Balocco et al., 2019). Yet, research has not

examined how lean startup can be successfully used as a tool for digital BMI in established

companies.

1.2 Problem discussion

In comparison to the dynamic environment of startups, established companies need to undertake

tremendous changes to shift from a traditional business to the digital economy (Ross et al.,

2016). Still, many of these large enterprises are in an early stage of adopting and implementing

digital technologies (Sebastian et al., 2017). Often, established companies lack organizational

structures and capabilities to make fast decisions and hence, have difficulties transforming

(Ross et al., 2016). Over the past years, the agile approach of business experimentation became

an emerging topic in BMI (Bocken & Snihur, 2020). However, this approach is mainly used in

startups and entrepreneurial contexts, given the dynamic and fast-changing environment. In line

with these business experimentations, agile methods are gaining more and more interest not

only for startups but also for established leaders. However, established companies seem to

struggle with adapting to the new, agile, and experimental work environment and fail to

implement methods like lean startup (Bocken & Snihur, 2020). Still, academic understanding

of lean startup is only emerging (Bocken & Snihur, 2020). Thus, it needs further investigation

to enrich current theory and discover enabling and hindering factors for applying lean startup

in established companies. As lean startup has proven to be a successful tool for BMI in (digital)

startups, we aim to identify success factors for established companies.

1.3 Research question and research purpose

The purpose of our study is to identify enablers and barriers of lean startup as a tool for digital

BMI in established companies. By doing so, we aim to propose a framework showing how

established companies can be successful in digital BMI using lean startup. Thus, the research

question which shall be explored is:

What are enablers and barriers of lean startup as a tool for digital business model

innovation?

To answer our research question and to build our framework, we conducted semi-structured

interviews with experts in lean startup and digital BMI, working in or with established


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companies that are shifting towards a digital BM. By doing so, we gained an in-depth

understanding of occurring barriers and enablers for lean startup in established companies given

our diverse interview partners. We identified internal and external factors facilitating and

hindering lean startup in established companies. Also, we outline necessary organizational

changes required to succeed in digital BMI with lean startup. Our study contributes to current

theory of agile methods and lean startup, BM, and BMI. Thus, we combine lean startup and

BMI, specifically digital BMI, by investigating lean startup as a tool for digital BMI and

extending it to established companies. Further, we provide practical implications on how such

companies can use lean startup or what to consider when already working with the approach.

1.4 Outline of the paper

The thesis is structured as followed. In chapter 2, the study’s theoretical background is outlined.

Namely our main research fields BMs, BMI, and lean startup as well as their connections

regarding the research purpose. Also, we show how digital technologies influence these areas.

In chapter 3 we describe the methodology and research design. We will provide an overview of

our sampling, data collection, analysis process, and illustrate how our empirical research met

quality criteria and ethical principles. In chapter 4 we present our empirical findings, by

outlining enablers and barriers for established companies when using lean startup as a tool for

digital BMI. Our findings are then analyzed in chapter 5. Highlighting main factors hampering

and facilitating lean startup in established companies and discuss those in the context of existing

theory to answer our research question and to propose our framework. Finally, we will conclude

our study in chapter 6 by showing findings, highlighting enablers and barriers when working

with lean startup in digital BMI, giving theoretical and practical implications, discussing

limitations to our work, and proposing further research.


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2. Literature Review

___________________________________________________________________________

This section examines the current state of literature regarding the phenomena under study.

The literature collection process is described along with a literature review of relevant topics,

including business models, business model innovation, and lean startup. ______________________________________________________________________________________________________________________________________________________

This study links different fields of research, specifically BM, digital BMI, and lean startup

approaches. The research question emerged from combining these research areas. To gather

relevant scientific articles and findings within these fields and to answer the research question

appropriately, different scientific databases, such as Web of Science and Primo, were used.

Further, to assure that only relevant and contemporary literature is used, we filtered our search

for the newest publications as well as most cited publications. Specifically, relevant academic

articles, used for this research, were collected by combining different search terms, like

“Business Model Transformation”, “Business Model Innovation”, “Digital Business Model

Innovation”, “Business Experimentation, “Lean Startup Approach” and “Established

Companies”. We also used relevant articles to identify further appropriate academic sources for

our study to assure high quality of research. More specifically, to get a deeper understanding of

specific theories and themes, we used backward citation searching. The purpose of the literature

review is to give an overview of relevant theories, definitions, and findings of our research

frame. Moreover, it serves as a fundamental grounding for the empirical part of our study.

2.1 Business Model Innovation

In this chapter, we are outlining the terminology of BMs and digital shift of BMs to address the

topic of BMI and further introduce the term digital BMI.

2.1.1 Business Models

The topic of BMs has gained much popularity in the research area of strategic business and

business innovation. Before the introduction of the BM concept, scholars used organizational

design or business strategy to describe aims and purpose of a company (Keen & Qureshi, 2006).

However, nowadays there is a need to distinguish between business strategy and BM, as

business strategy rather defines how a company is making business. Opposing to that, BM

answers the question of what a company is doing (Keen & Qureshi, 2006). Still, these two

concepts are closely linked as strategic analysis of companies is an inevitable element for BM

design (Keen & Qureshi, 2006; Teece, 2010). Every firm is grounded in a BM, even if the

company itself does not express it directly (Chesbrough, 2007; Foss & Saebi, 2018; Teece,


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2010). Many academics contributed to defining the term BM, however, there is still no mutually

used definition (Foss & Saebi, 2018). Nevertheless, most scholars have agreed that BMs

function to create, deliver, and capture value (Chesbrough, 2007; Foss & Saebi, 2018;

Osterwalder & Pigneur, 2010; Teece, 2010).

BMs are seen as a way to create value for customers and how the business turns market

opportunities into profit through different actors, activities, and collaborations (Rajala &

Westerlund, 2005; Zott & Amit, 2007). Afuah (2004) describes BMs as a set of activities a

company performs, how it performs those and when it performs them. They represent all

elements of a company’s business activities, their relationship, and interaction (Rachinger et

al., 2019). Correspondingly, BMs define the building blocks of value chain, creating value by

defining a set of activities from raw materials through to final consumers. BMs also outline

structures of revenues, costs, and profits of a company when delivering value (Teece, 2010).

Osterwalder and Pigneur (2010) created a tool, named Business Model Canvas, which visually

illustrates nine building blocks of any BM. Namely user segment, value proposition, channels,

user relationships, revenue streams, key resources, key activities, key partners, and cost

structure. Because of the more customer-driven world, companies are obliged to rethink their

business strategy and to complement revenues with a suitable BM to respond to the two main

dimensions of BMs, specifically value creation and value capturing (Amit & Zott, 2012b;

Baden-Fuller & Haefliger, 2013). Value creation describes the identification of customers and

their engagement with a company, whereas value capturing marks how novel value is delivered

to customers and how money is made with it (Baden-Fuller & Haefliger, 2013).

2.1.2 The digital shift – Business Models and Digital Technologies

Digital technologies are directly linked to the development and transformation of BMs as they

change how firms produce and deliver value (Keen & Qureshi, 2006; Vaska et al., 2020). They

create value in a new way for businesses, optimize customer experience, and build new

capabilities which support overall business activities (Dörner & Edelman, 2015). Thus, they

make it necessary for companies to undertake a digital shift. In our study, we define digital shift

as the transformation of a company’s existing BM towards a digital BM by adopting digital

technologies. By doing so, digital technologies influence business activities by advancing BMs

or even making former successful BMs now obsolete (Kiel et al., 2017; Tongur & Engwall,

2014). Baden-Fuller and Haefliger (2013) reflect on the correlation of digital technologies and


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BMs, indicating to which extent and when digital technologies require changes in BMs to gain

a competitive advantage. The authors claim that digital technologies and BMs interact

constantly in a two-sided way (Baden-Fuller & Haefliger, 2013). Hence, the choice of a specific

BM can determine how digital technology can be made profitable, but also digital technologies

can influence the possibilities of BMs. Consequently, focus on customer engagement as well

as value creation is increasing. To capture this novel creation of value, digital technology needs

to be intertwined with the right BM (Baden-Fuller & Haefliger, 2013; Teece, 2010).

Integrating digital technologies into BMs reshapes and improves customer value propositions

and leads to greater customer interaction and collaboration (Berman, 2012). Thus, it brings

opportunities across the entire organization. As a result, digital technologies increase the speed

of change and lead to transformations across industries (Bharadwaj et al., 2013).

Implementation of digital technologies is completely reshaping traditional business strategies

as business practices can now be carried out across boundaries of time, distance, and function

(Banker et al., 2006; Ettlie & Pavlou, 2006; Kohli & Grover, 2008; Rai et al., 2012;

Sambamurthy et al., 2003; Straub & Watson, 2001; Subramaniam & Venkatraman, 2001;

Tanriverdi & Venkatraman, 2005; Wheeler, 2002). In this context, digital technologies are seen

as an influence on three dimensions: (1) externally, meaning to digitally enhance customer

experience, (2) internally, meaning to influence business operations, decision-making, and

internal structures and (3) holistically, stating that all business segments are being affected,

which leads to completely new BMs (Hess et al., 2016, 2020; Kaufman & Horton, 2015;

Schuchmann & Seufert, 2015). Hence, digital technologies can change individual business

elements within a BM, the complete BM, adding on different components of value chains, or

the recombination of networks of different actors within a BM (Schallmo & Williams, 2018).

To what extent a BM will be changed depends on the degree of implemented digital

technologies.

It is argued that digital technologies bring new opportunities for the conceptualization of BMs

as well as novel forms for organizations to create and capture value (Vaska et al., 2020).

Opposing to that, drastic changes resulting from the emergence of novel digital technologies

(e.g. mobile technologies, cloud computing, big data, etc.) make it extremely hard for

established companies to stay ahead of competitors that are born-digital and familiar in handling

novel digital technologies. According to Piccini et al. (2015), traditional structures of whole


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industries are being transformed with the introduction of novel digital technologies, which

contains many challenges concerning BMI. However, the new digital environment forces all

companies to adapt to these changes brought by digital technologies to stay competitive

(Subramanian et al., 2011). Especially for established companies, this can be a challenging task.

2.1.3 Digital Business Model Innovation

BMs are capturing how value is created and delivered (Chesbrough, 2007; Foss & Saebi, 2018;

Osterwalder & Pigneur, 2010; Teece, 2010) and digital technologies in this context are a great

source of innovation and an enabler for advancing and developing BMs (Rachinger et al., 2019).

However, the highly competitive and rapidly changing business environment today implicates

that BMs became a significant factor for competitive advantage (Berends et al., 2016) and are

also an important source of innovation (Rachinger et al., 2019). Therefore, it is beneficial for

companies to change their process of value creation and capturing (Berends et al., 2016) and to

develop appropriate capabilities to innovate their BMs (Chesbrough, 2010). Berends et al.

(2016) describe BMI as a “multi-step, multi-mechanism learning process” (p. 200), meaning

that it is rather a loose trail-and-error-process of making changes and adjustments, and learning

from these to develop new BMs. However, BMI does not only result in completely novel BMs

and can also be the outcome of innovating existing ones (Rachinger et al., 2019).

Often, novelty and efficiency are key aspects named in BMI (Amit & Zott, 2012b). However,

in large enterprises, this expectation can conflict with traditional setup and structures as

managers see existing business threaten and are therefore less willing to experiment

(Chesbrough, 2010). There is a conflict between existing and new business (Berends et al.,

2016; Christensen, 2003) that makes BMI challenging for established companies, also referred

to as ambidexterity of exploitation and exploration (O'Reilly & Tushman, 2004). The term

ambidextrous organization describes companies that maintain and protect their established

business while giving space for new, disruptive innovation beyond their existing products and

services (O'Reilly & Tushman, 2004). In this context, existing structures can both facilitate or

hamper BMI, as resources might be useful for innovation processes, but existing structures

could also lead to inertia as opportunities might not be perceived as such (Berends et al., 2016).

Thus, it is challenging to balance between exploiting existing businesses while exploring

potentials for innovation growth (O'Reilly & Tushman, 2004). Hence, established companies

need to identify what made them successful in the past and predict what can work in the future,


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to make appropriate changes regarding their BM. The difficulty is to understand the complexity

of BMs and to make right assumptions about different elements of a BM and their interactions

(Berends et al., 2016). Also, it is hard to predict whether or not a BM will be successful (Berends

et al., 2016). Generally, it is key to identify and understand barriers of BMI within the

organizational context and to make necessary changes in leadership, culture, and organizational

structures accordingly (Berends et al., 2016; Chesbrough, 2010). Therefore, it is important for

established companies to learn through experimentation (Chesbrough, 2010) and to build an

effective learning process to be innovative and to create novel BMs (Berends et al., 2016). Also,

it is necessary to understand, that experiments can and will fail and that it requires an open

culture allowing such failures to learn from them (Berends et al., 2016; Chesbrough, 2010).

According to Chesbrough (2010), it requires internal leaders to drive experimental change in

BMI to create new and better value propositions. Coming back to the phenomenon of

ambidexterity, it is crucial to drive cultural change while maintaining existing businesses and

to find a solution how both can coexist and later be combined within a company (Chesbrough,

2010).

As we argue that established companies need to undertake a digital shift of existing BMs by

adopting digital technologies, we define digitization of BMs as digital BMI. In this context, the

influential character of digital technologies on BM has three different dimensions: (1) Digital

technologies can lead to optimization of existing BMs, meaning cost efficiency by using digital

technologies for instance. In this context, optimization refers to digitization of products and

services. (2) Digital technologies can transform current BMs, meaning an extension of existing

BMs or changing individual elements by adopting digital technologies. Thus, they digitize

processes and decision-making. And (3) digital technologies can empower the development of

novel BMs, that either replaces or accomplishes existing ones by completely changing value

propositions (Berman, 2012; Matzler et al., 2016; Rachinger et al., 2019). Therefore, we define

the three dimensions of digital BMI as (1) optimization, (2) transformation, and (3) novel BM

creation, meaning that established companies are to some extent adapting and implementing

digital technologies in their BMs.

2.2 Lean Startup as a Tool for Digital Business Model Innovation

In the field of digital entrepreneurship, lean startup has already been used as a tool to validate

and innovate BMs (Ghezzi, 2020) and is a scientific approach for a fast go-to-market strategy


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(Tohanean & Weiss, 2019). In the following, we introduce lean startup as an agile method for

digital BMI and further bring it into the context of established companies.

2.2.1 Business Experimentation and Lean Startup

Lean startup can be used to advance innovation processes by bringing lean thinking into

organizations (Balocco et al., 2019) and to continuously reinvent BMs. Such a task requires

experimentation and customer involvement (Xu & Koivumäki, 2019), which are essential

aspects covered in lean startup. Business experimentation is a new, emerging field in BMI, and

it is argued that experimental learning significantly influences innovation processes (Berends

et al., 2016; Weissbrod & Bocken, 2017). Meaning, that organizations that constantly

experiment with innovative ideas and concepts are more likely to be successful with the intent

(Chang et al., 2012). Build-measure-learn cycles (Ries, 2011) enable every size of company to

think and operate like a startup and convert ideas into feasible products and services. By using

this approach, it is less about following a clear plan and rather doing a trial-and-error experiment

(Xu & Koivumäki, 2019). Research revealed that business experimentation not only enables

learning and innovation but also reduces uncertainty, can help to interact and communicate with

customers and other stakeholders, and also helps to overcome organizational inertia (Bocken &

Snihur, 2020). Thus, BMI needs to be seen as an ongoing process that requires appropriate

mechanisms (Tohanean & Weiss, 2019).

2.2.2 Lean Startup

In 1997, the term lean philosophy was introduced by Womack & Jones (1997) initiated with

novel development to put high emphasis on customers’ interest. Novel customer value at that

time changed production systems and business activities in the entire construction industry.

Inspired by the Toyota Production System, where it is all about reducing waste in industrial

processes, the authors introduce „five principles of lean”, namely (1) create value for customers,

(2) identify value streams, (3) create flow, (4) produce only what is pulled by customers, and

(5) pursue perfection by continuously identifying and eliminating waste (Womack & Jones,

1997). Those principles demonstrate a high focus on customer value that is implied by the lean

philosophy (Ghezzi & Cavallo, 2020). It also shows a customer or user-centric approach.

Ries (2011) and Blank (2013) firstly transferred lean philosophy from the manufacturing

industry to the entrepreneurial world. The idea of lean startup evolved when Eric Ries was

confronted with the failure of his products getting traction (Leoveanu, 2018). As an engineer,


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he figured that the problem had to do with technical problems of the products. However, it

turned out that he just spent a lot of time building things nobody was interested in (Ries, 2011).

With this experience, he was not alone, as 75% of startups failed in the initial startup phase due

to traditional approaches (Blank, 2013). The commonly known manner to create a BM was to

build a business plan with a five-year prospect, which was pitched to investors. If the pitch was

successful, the business plan would be realized and brought to market. Only then would

entrepreneurs get feedback from customers about products or services. This means,

entrepreneurs would find out if the initial business idea was a hit or miss, only after a product

was already available on the market when lots of time and money was already spent (Blank,

2013). So, there was a need to change this process, making the creation of novel BMs less risky.

As a solution, different lean startup approaches have been introduced (Ghezzi & Cavallo, 2020),

such as lean startup (Ries, 2011) and customer development methods (Blank, 2013). Ries

(2011) and Blank (2013) define lean startup approaches as a startups’ way of identifying all

preferred features of a business idea, activities, and processes of customers and which are non-

relevant, or even not wanted. To reduce uncertainty about a business’s viability, startups need

to iterate their business ideas as quickly as possible applying the method of learning-by-doing

(Gans et al., 2019; Ott et al., 2017). Getting the right business idea quickly to market has a

positive impact on its success in the long run (McDonald & Gao, 2019; Zott & Amit, 2007).

Therefore, lean startup is a useful tool helping to iterate business ideas quickly, up to the point

where entrepreneurs are able to formulate a sound BM and make early assumptions about its

feasibility (Blank, 2013; Ries, 2011). The method contains two main features. Firstly,

formulation of hypotheses in nine areas of a business idea (called Business Model Canvas) and

secondly, the approach of getting-out-of-the-building, meaning that each hypothesis will be

tested by interviewing customers and other stakeholders (Osterwalder & Pigneur, 2010; Ries,

2011). The Business Model Canvas is used to visually illustrate the complete extent of a

business, its goods, services, parties involved, and how ownership of goods/services will be

exchanged. Each of the nine building blocks withholds assumptions about the business idea and

is being used as hypotheses to test BMs (Leatherbee & Katila, 2020). The goal is to test all

hypotheses with potential customers to build a strong BM with (dis)confirmed hypotheses

(Leatherbee & Katila, 2020). The canvas supports lean startup as it causes entrepreneurs to ask

the right questions, such as: Who are the customers?, What are you building for them?, What

is the value proposition?, What are the right channels to reach your customers?, How do you


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keep existing customers and how do you get new ones?, What are your pricing tactics?, What

is your revenue model?, What resources do you need and which partners do you need? (Blank

& Euchner, 2018). Traditionally when building a BM with the canvas, building blocks were

filled in as facts, without testing them. With lean startup, the fields are being filled with

hypotheses that are being tested out-of-the-building with potential customers (Blank &

Euchner, 2018). The method makes initiation of a new BM less risky, as it uses experimentation

instead of planning, valuable customer feedback instead of making assumptions, and intuition

and an iterative design process instead of traditional time-consuming and costly perfectionistic

design upfront (Blank, 2013). Lean startup, therefore, reduces uncertainty about a business

idea’s viability and helps to indicate if there is an actual customer need as well as to quickly

decide whether to stick with a BM (persevere), to change/adapt it (pivot), or not proceed with

the idea (perish) (figure 1).

Figure 1: Lean startup hypothesis process (Eisenmann et al., 2012).

Lean startup makes use of a hypothesis-driven attempt, in which innovators and entrepreneurs

formulate their business intent into measurable hypotheses. These hypotheses are incorporated

into a lean startup Business Model Canvas and can furtherly be combined with formulation of

user stories as well as use cases and first estimate business cases. In a following step, these

hypotheses are being tested with potential customers. If an entrepreneur decides to persevere

with a BM, it will then be translated into a minimum viable product (MVP). Eisenmann et al.

(2012) define a MVP as the smallest set of activities needed to disprove a hypothesis. Ries


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(2011) describes it as a product that combines just a few features which makes it possible for

early adopters to give useful feedback for further product development. Early adopters are

involved in hypotheses testing through experiments with a MVP, where they give feedback

while using it. This process is highly valuable for the development, as a product is based on

customer needs, opposed to the approach of basing evaluations on secondary data or desk

research or even pure assumptions (Ghezzi & Cavallo, 2020). Based on experiments’ results

with early adopters and lead users, hypotheses can be proven right or wrong. Continuous testing

and improving are referred to as build-measure-learn loops (Ries, 2011). In these loops (figure

2) a MVP is built (Build), then customer feedback is given and evaluated (Measure) and from

those insights, learnings are being generated (Learn).

Figure 2: Build-Measure-Learn loop (Ries, 2011).

MVPs, as well as BMs, will be adapted according to customers’ preferences (Ries, 2011). Thus,

a MVP is used to validate or invalidate assumptions (Blank & Euchner, 2018). This process is

repeated until all hypotheses are validated through MVP testing. According to Eisenmann et al.

(2012), at that point, a product or service achieves its approved product-market-fit. In addition,

this process helps not only to validate hypotheses but also to improve products as such. Nobel

(2011) additionally highlights the emphasis on speeding up the process, as with lean startup

companies do not need to spend several months perfecting a full-featured product but rather

launch a quickly developed MVP. Therefore, build-measure-learn cycles shall be executed

quickly to fail early and to succeed sooner (Brown & Katz, 2019; Ries, 2011). In turn, MVPs

help companies to quickly market products that are highly relevant for their customers and have

a proven product-market fit.


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Many startups have succeeded with lean startup (Leatherbee & Katila, 2020), such as Dropbox

and Zappos, however, it still has some limitations. Ladd (2016) argues that despite its well-

deserved popularity, the method requires strict tailoring and needs to be used with care. It is

crucial to generate unique and testable hypotheses, which can be challenging as lean startup

does not per se guides through the process (Bocken & Snihur, 2020). Literature states that

continuous testing of multiple hypotheses decelerates the process and leads to disheartened and

impatient founders (Ladd, 2016). Moreover, it is argued that the customer-centric approach

presents some pitfalls, as it is difficult to capture non-biased and valuable customer feedback

to confirm hypotheses (Felin et al., 2019; Ng, 2014; Nirwan & Dhewanto, 2015). Also, heavy

reliance on customer feedback is harshly criticized, as customers might not necessarily have

better knowledge of a business idea or product (Felin et al., 2019). The customer-centric

approach also leads to focus on what customers want today and fails to predict what will occur

in the future (Mollick, 2019). Additionally, novel business ideas are always harder to

understand for customers and tend to be initially disliked by them (Christensen et al., 2013). It

is even argued that regulatory and administrative barriers hinder the process of obtaining

customer feedback and as a result slow down the whole BMI process (Nirwan & Dhewanto,

2015). Therefore, it is questionable if lean startup helps to generate radical BMs and hence, is

rather a tool for incremental change (Felin et al., 2019; Mollick, 2019). Another critical aspect

is handling MVPs, as established companies often hesitate to create a perceived inferior

product, that might fail to satisfy existing customers’ needs (Nirwan & Dhewanto, 2015).

Opposingly, according to Zuzul and Tripsas (2020) entrepreneurs that strongly rely on their

original business idea might be less successful than entrepreneurs that continuously update their

BM, which is a favorable aspect supporting lean startup. Also, it is argued that a BM idea can

be both radical and incremental when applying lean startup, as it is not a tool to generate novel

business ideas but rather to test if an idea is solid (Bocken & Snihur, 2020). Despite the

skepticism (Felin et al., 2019; Ladd, 2016; Nirwan & Dhewanto, 2015), lean startup has been

adopted around the world as a popular solution to avoid business failure (Nirwan & Dhewanto,

2015) and is a successful tool for BMI (Leatherbee & Katila, 2020). Opposing to its popularity

in the business context, lean startup has not been thoroughly investigated in empirical research

(Leatherbee & Katila, 2020). Hence, it is necessary to fill this research gap and to examine how

established companies can successfully use lean startup as a tool for digital BMI.


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2.2.3 Digital Business Model Innovation with Lean Startup in Established Companies

As startups offer new products and BMs at a high speed, they put traditional actors and

especially established companies under high pressure (Edison et al., 2018). Startups are

constantly in close collaboration with potential customers, indicating gaps in existing market

offers to find new, scalable BMs. This means, for established companies in order to compete

with these disruptive players, they need to find new ways of creating and capturing value (Rejeb

et al., 2008). Therefore, a high focus for established companies is put to innovate and act like

startups to regain leading positions as well as to compete with startups. They realize that startups

are no longer ankle-biters, but serious competitors that can take over their business or even to

disrupt entire markets and that there is a need to rethink their BMs (Blank & Euchner, 2018).

Thus, established companies start adapting startup techniques to compete with startups, but

oftentimes fail to implement tools as an end-to-end solution for growth. Rather they use it for

experimentation but are not capable to bring new BMs to scale and market (Blank & Euchner,

2018). Especially for established companies, digital BMI can be a huge challenge as they might

not be used to the fast-paced digital environment. Therefore, business experimentation is

needed to react faster to new market conditions by continuously creating and testing hypotheses

about novel or changed BMs (Bocken & Snihur, 2020).

Especially, because established companies rely on and aim to fulfill existing customer needs

while simultaneously gaining new customers, the customer-centric approach of lean startup can

help them to do both. In this way, they centralize all their efforts to their main and necessary

business operations, focusing on what customers want and need (Ries, 2011). Through low-

cost experimentations with MVPs, new ideas can be tested easily and fast with customers, and

thus implement a startup mentality in large enterprises (Bocken & Snihur, 2020) without

making huge investments or long-time planning (Mansoori, 2017; Ries, 2011). Moreover,

continuity is a major advantage of lean startup as a tool for BMI. As pointed out before, it is

important for any company to continuously rethink its BM to stay competitive. With feedback

loops, as an essential part of lean startup, established companies can constantly challenge and

reinvent their business. In this context, it is important to truly understand customer needs and

work closely together with them to generate learnings from customers rather than selling them

something (Mansoori, 2017; Xu & Koivumäki, 2019). Ghezzi (2020) outlines the importance

of simple heuristics instead of abstract guidelines to successfully apply lean startup and make

use of existing resources. Further, lean startup requires agility on every level in terms of culture


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and organizational structure (Ghezzi & Cavallo, 2020) as BMI or change in general, especially

grounded in digital technologies, forces companies to adapt in the way they think and act. Such

agile structures can be found in (digital) startups (Ghezzi & Cavallo, 2020) and thus needs

further investigation in established companies that are facing a digital shift. Xu and Koivumäki

(2019) explain challenges of lean startup with an enormous change from existing procedures to

agile methods. Before, companies predicted what customers want. Now, by including them in

the process it shifts to customers telling companies what they want, thus altering the competitive

landscape and forcing companies to be primarily fast and agile. Therefore, organizational

structures and operational processes need to allow the inclusion of customers as external

sources (Mansoori, 2017). However, once an innovative idea is generated, the lean startup

process can yield novel and innovative BMs (Bocken & Snihur, 2020) and the lean perspective

can help to shift learnings from a small task to an organization-wide implementation in a step-

by-step process (Balocco et al., 2019).

According to Ries (2011), the core idea and activities of lean startup offer many benefits for

established companies, which is why lean startup has gained interest from companies, such as

General Electrics, 3M and W.L. Gore, etc. (Edison et al., 2018). According to a study by the

Harvard Business Review on 170 corporate executives, 82% of them are using elements of lean

startup in their business context (Kirsner, 2016). Still, many large companies struggle with lean

startup. One critical aspect is that they fail to connect innovation teams with operational groups

(Blank & Euchner, 2018). Another problematic factor is that oftentimes established companies

have managers with a rather traditional leading styles that fail to understand the needs to apply

lean startup thinking within a company (Blank & Euchner, 2018). Also, employees of large

organizations are motivated for different reasons as startup employees, who are rather

motivated by passion and drive to make a difference (Blank & Euchner, 2018). Nevertheless,

lean startup is nowadays one of the most common and trusted innovation methods not only by

startups and entrepreneurs but also for corporations and policymakers (Leatherbee & Katila,

2020).

However, scientific and empirical insights about using lean startup in established companies

are still rare. Hence, there is an urgent need to understand how to successfully use lean startup

in those companies (Edison et al., 2018). Thus, we aim to identify enablers and barriers

influencing usage of lean startup as a tool for digital BMI in established companies.


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3. Methodology

___________________________________________________________________________

This chapter provides an overview of our research environment outlining the research’s

underlying philosophy, our research approach, and design, as well as data analysis including

quality insurance and ethical considerations. ______________________________________________________________________________________________________________________________________________________

3.1 Research Philosophy

Research philosophy consists of the ontological and epistemological view of a study. Ontology

describes basic assumptions researchers hold about the nature of reality and existence, basically

how we see the world (Easterby-Smith et al., 2018). For this study, we ground our assumptions

on relativism. Relativism defines reality as a subjective product of how individuals see the

world, as reality depends on individual perspectives, and truth is created subjectively by

individuals (Easterby-Smith et al., 2018). This perspective suits our study best as we generate

our data from interviews with diverse experts, with different knowledge, experience, and

expertise in lean startup for digital BMI and each one perceives reality differently. Furthermore,

we generate our knowledge through our observations and gathered data from interviews and

hence, build a multi-sided truth of the investigated topic. In relativism, scientific laws are not

predefined but created by people (Easterby-Smith et al., 2018). Hence, we explore

heterogeneity of opinions on our research themes digital BMI and lean startup, as we also

include different standpoints and perspectives, due to the different roles of the respondents.

Epistemology describes how knowledge is generated in terms of how researchers enquire the

physical and social world, or simply how we know what we know and how we investigate the

world (Easterby-Smith et al., 2018). Based on our relativistic ontological viewpoint, our study’s

underlying epistemology is social constructionism. Social constructionism aligns with the

perspective that reality and knowledge are created by people and it is important to acknowledge

individual perspectives (Easterby-Smith et al., 2018). We base our findings and emerging

knowledge on our interviewees’ experience and therefore, need to see reality in different ways

even though we aim to develop a common understanding by identifying certain patterns and

similarities. From a social constructionism position, we as researchers are reflective observers.

Thus, we take active part in the research by interacting with interview partners, interpreting our

findings, developing deeper understanding of the topic, as well as explaining and generalizing

the research phenomenon and its outcome. By doing so, we gather rich data about lean startup

as a tool for digital BMI in established companies and construct an understanding of the


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research fields lean startup and digital BMI to later generalize our knowledge through

theoretical implications.

3.2 Research Approach

Based on our research philosophy we conducted an exploratory, qualitative research.

Coherently with our ontology and epistemology, we choose grounded theory as the underlying

methodology, following an abductive approach. Thus, we aim to collect and analyze data by

identifying common patterns to extend existing theory and eventually generating new ones

(Saunders et al., 2012). In comparison to deductive or inductive research, the abductive

approach allows us to explain, develop, and change the theoretical framework at any time in

the research process (Saunders et al., 2012). It is a mixed approach of inductive and deductive

that aims to derive novel insights from gathered data to contribute to existing theory and thus

emphasizes researchers to acquire a deep understanding of the topic beforehand (Timmermans

& Tavory, 2012). As we investigate lean startup as a tool for digital BMI, we intend to

contribute to and extend current theory about lean startup and BMI. Thus, we combine fields

of lean startup and BMI, extending BMI in the digital context and investigate both in a new

context within established companies.

Therefore, we choose grounded theory by Charmaz (2000) as it aligns best with our relativism

and social constructionism viewpoint, as well as the abductive approach. As we aim to identify

enablers and barriers of using lean startup for digital BMI in established companies, we consider

different perspectives of our interviewees due to different roles within or without company

boundaries, years of experience, and expertise of each respondent. We used literature to get

familiar with lean startup and digital BMI to design our theoretical framework by combining

these fields and putting them in the context of established companies. However, we did not use

any pre-assumptions regarding our research purpose when conducting the research and

analyzing the data. In general, grounded theory examines a research problem without making

prior assumptions where theory emerges from data by continuously comparing it with each

other to identify common patterns (Easterby-Smith et al., 2018). However, Charmaz’s (2000)

approach allows and recommends prior familiarization with literature to get a pre-

understanding of the topic and thus to better examine data later in the research process.

Compared to other approaches of grounded theory, the main difference in Charmaz’s (2000)

approach is that results should emerge from the interaction between research subject and

researcher. Therefore, we understand our role as reflective observers and co-creators of


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meaning by interacting with our interview partners. Rahmani and Leifels (2018) show how an

abductive approach can be used with grounded theory arguing that even though the literature

review is influencing the research question, there is no need to direct data collection and

analysis towards any pre-defined theories. Similar to our study, we developed our research

question based on the identified gap in literature but not used existing concepts to analyze our

data, rather we aimed for “surprising facts” emerging from data (Saunders et al., 2012).

3.3 Research Design

3.3.1 Data Collection

The collected qualitative data consist of primary data, which is conducted through semi-

structured interviews with diverse experts of lean startup usage for digital BMI. Aligning with

our study’s purpose, to understand enablers and barriers of lean startup for established

companies, we collected data from ten in-depth, semi-structured interviews. The interviewees

are all experts in lean startup as a tool for digital BMI in established companies, as they all have

been or are working in/with established companies using lean startup. A detailed description of

our experts, their roles, and experience with lean startup as a tool for digital BMI in established

companies, is shown in table 1.

We used in-depth, semi-structured interviews, as we want to explore deeper meanings, patterns,

and relations of different fields concerning the usage of lean startup for digital BMI in

established companies. As most of the experts work in different industries with different roles

and perspectives (table 1), we thrive to cover as many aspects as possible for using lean startup

in different established companies. Therefore, semi-structured interviews appear as the most

appropriate method. Moreover, we wanted to be flexible with questions throughout the

interviews, to make sure to react and cover areas that we initially did not think about. Thus,

semi-structured interviews allow to make answers comparable but still give space to

spontaneously react to responses and to ask follow-up questions (Easterby-Smith et al., 2018).

We did not conduct completely structured interviews, as they tempt us to make assumptions

before having the interviews as well as to leave out important insights, which we do not cover

with our planned interview questions. Also, as we aim to explore which enablers and barriers

established companies face when working with lean startup, we refrain from working with

unstructured interviews. Without giving any guidelines and setting a frame, we would not

collect useful and appropriate data on our intended research, which is why we choose to have a

semi-structure (Easterby-Smith et al., 2018).


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Table 1: Overview of Interview Partners.

Interviewee Job Description Industry External/

Internal

Perspective

Date Length Background & Experience with Lean

Startup

Respondent 1 Project &

Innovation

Manager &

Product Owner

New Business

Logistics Internal

perspective

30.03.21 58 min Uses Lean Startup method for the last six

months for an internal intend to create novel

digital business models within the boundaries

of an established company.

Respondent 2 Management

Consultant, Agile

Coach & SAFe

5.0 SPC

Consulting External

perspective

01.04.21 65 min Works as an external coach for established

companies that are using the lean startup

method for digital business model innovation.

Respondent 3 PhD. Advisor and

Lecturer on

Business Model

Innovation and

Customer Value

Creation

Consulting/

Education

External

perspective

02.04.21 70 min Acts as an external coach for business model

innovation for 13 years. Teaches business

model innovation with lean startup for the last

six years.

Respondent 4 Agile Coach and

Digital Consultant

for digital

Transformation

and New Work

Consulting External

perspective

06.04.21 62 min Acts as a digital coach for the last nine years,

mainly for established companies that are in the

process of digital transformation. Has had one

big project for two years where he consulted an

established firm in the process of digital

transformation with the help of the lean startup

method.

Respondent 5 Innovation Lead Production

(B2B)

Internal

perspective

06.04.21 66 min Acts as lean startup ambassador for three years.

Was commissioned by management of an

established firm to work as an innovation lead

with lean startup in order to create novel digital

business models and to digitally transform the

traditional and existing business models of the

established company for the last two years.

Respondent 6 Innovation

Manager

Consulting External

perspective

08.04.21 63 min Acted as an external coach for one year for

established companies that practice (digital)

business model innovation and used lean

startup method in this context.

Respondent 7 Innovation and

Technology

Consultant

Consulting External

perspective

08.04.21 58 min Works as an external consultant for different

traditional companies that are in the process of

digital transformation and supports them with

digital business model innovation with lean

startup method.

Respondent 8 PhD.

Organizational

Development

Consultant

Corporate

Incubation

Automotive

Industry

(Innovation

Lab)

Internal

perspective

(Corporate

incubation)

16.04.21 61 min Set up and worked for almost four years as a

business accelerator for the corporate

organizational development in form of an

innovation lab of a big corporate automotive

group. There, different initiatives for digital

business model innovation were practiced with

the lean startup method.

Respondent 9 Organizational

Development

Consultant

Corporate

Incubation

Automotive

Industry

(Innovation

Lab)

Internal

perspective

(Corporate

incubation)

21.04.21 56 min Colleague of Respondent 8. Worked for three

years at the innovation lab of a big corporate

automotive group. Different initiatives for

digital business model innovation were

practiced with the lean startup method.

Respondent 10 Founder of

Innovation Hub;

Agile Coach

Innovation

Innovation

Hub

External

perspective

23.04.21 59 min Background in a digital factory incubation lab

of a big automotive group. Founder of an

innovation hub where organizations are

supported to master innovation in a digital

world since one and a half years. Works with

different agile methods, inter alia Lean Startup,

to develop digital business models, products

and services for and with established medium-

sized enterprises.


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3.3.2 Sampling Strategy

As a sampling method, we used non-probability sampling with purposive sampling, as we aim

to identify reasonable specimens of the researched phenomenon (Easterby-Smith et al., 2018).

As our research purpose is to understand enablers and barriers of lean startup for established

companies, we need experts that are familiar with the research topic, that have broad experience

and knowledge within this field. Meaning, that only a small collection of individuals has the

required expertise, in working with lean startup as a tool for digital BMI, which we can choose

from. With probability sampling methods the data sample could be biased, as not everybody

has expertise and experience working with lean startup for digital BMI in established

companies.

To get in contact with potential interviewees, we first did online research on professionals

working with lean startup for digital BMI in established companies. Therefore, we searched for

the keywords “Lean Startup” and “Business Model Innovation with Lean Startup” on Google

and LinkedIn. From there we identified several persons that appear to be experts in using lean

startup for digital BMI. To detect if a person was specifically suitable for our study, prior we

set different criteria for selecting our interviewees. First, the person needed to have knowledge

and a deep understanding of lean startup. To ensure this criterion, we checked their working

experience with lean startup, and we only contacted individuals if they directly stated that they

are/have been working with lean startup. All our experts explicitly mentioned in their online

communication, either on their LinkedIn profile or on the respective company website, that they

have been/are working with lean startup. Some experts are moreover acting as lean startup

ambassadors or are teaching lean startup. This is how we made certain that they have deep

understanding and knowledge within the field of interest. Another criterion which we set, was

that the experts understand lean startup as a tool for BMI, and not only as an agile working

method as such. To ensure this criterion, we asked the experts how they work with lean startup.

We only set an interview, if the individuals stated that they use it as a tool for BMI. Furthermore,

we made sure that the experts use lean startup for BMI in companies that are facing a digital

shift, meaning that the experts have experience with using lean startup for digital BMI. This

leads to another criterion that we set priory. As lean startup is commonly known as a method

for startups, we needed to make sure that the experts work with established companies and not

for/with growing companies and startups. To ensure this we asked the experts directly for/with

which types of company(ies) they use lean startup. Again, we only scheduled interviews with


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experts that explicitly mentioned that they use lean startup in/with established companies. Also,

it was interesting for us, to understand if a person acts as an external coach or consultant, uses

lean startup within company boundaries, or in-between both positions, e.g. in a corporate

incubation position. This way, we made certain that we cover different perspectives of using

lean startup to get a holistic understanding. Only if a person corresponded to all above-named

criteria and was indicated to be suitable for our research intend, we scheduled an appointment

for an expert interview.

To avoid sampling bias, we aim to make data collection as representative, yet still comparable,

as possible (Easterby-Smith et al., 2018). For this, we chose diverse interviewees, that all work

with lean startup for digital BMI in established companies, but in different positions and

perspectives (table 1). Meaning, that we interviewed experts that function as external

consultants as well as employees working with lean startup as a tool for digital BMI and people

that function as innovation leaders, supervising usage of lean startup for digital BMI. Also,

some of the experts act as corporate incubators, for instance in corporate innovation labs,

meaning they work independently in parallel to the core business of established companies to

innovate existing BMs. Thus, we have different perspectives (internal and external) on using

lean startup as a tool for digital BMI. This helps us to critically reflect on empirical findings.

Additionally, all interviewees have different years of experience within the field. Moreover, all

experts work in different markets, reaching from the automotive industry, finance, to production

and manufacturing. Still, all respondents work for/with established companies in Germany that

were founded before the dotcom bubble (1995-2000) and are now facing a digital shift of their

existing BMs.

3.3.3 Interview Design

We conducted ten semi-structured interviews between 30th of March and 23rd of April 2021

using an interview guide (appendix 1) containing a set of pre-defined questions. The interviews

lasted on average one hour and thus, we gathered more than ten hours of interview recordings

(table 1). We did not specify an explicit number of interviews that we planned to conduct, as

we aimed for knowledge saturation. This was achieved with the 9th interview and confirmed

with the 10th, as no new data derived.

The interview guide was developed with different fields of our research purpose and key

findings from the literature review. Also, the interview guideline was tested with one expert


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before the planned interviews to detect unsuitable topics and to see if the interview guideline

works in practice. This expert was a suitable choice to test the interview questions, as the expert

has been recently using lean startup for digital BMI in an established company. There the expert

started several projects, applying lean startup, to create novel, digital BMs. The company was

founded in 1885, is very conservative, and is slowly proceeding in the digital transformation

process, which makes it fit perfectly to our criteria. Also, the expert has theoretical knowledge

from his academic background. From the feedback of the test respondent, we learned that some

questions were still too narrow and needed to be rephrased to not bias the interview outcome.

Also, it was a helpful trial on how to lead respondents through the interview guide.

The interview guide covers two main topics: First, general understanding of the topic and how

the respective companies use lean startup and secondly, the experts’ experience with lean

startup as a tool for digital BMI in established companies. During the interviews, we asked our

experts four open questions about their experience with lean startup as a tool for digital BMI,

two questions about positive aspects as well as three questions concerning which barriers and

difficulties they experienced and how they dealt with them. Also, we asked six questions about

advantages and drawbacks of lean startup as a tool for digital BMI in established companies.

As we used a semi-structured interview guide, during the actual interviews we adapted some

additional open questions (see optional questions in appendix 1), which came up spontaneously

as a reaction to the respondents’ experiences. Accordingly, we added some questions

concerning the experience with handling digital BMI in general and the potential for

improvement.

All interviews were conducted digitally, using online video platforms, namely Zoom and

Microsoft Teams, as those were our respondents’ preferred platforms. This approach was most

suitable, as the respondents were all spread around different locations in Germany. All

interviews were conducted in German, transcribed with the software trint.com, and then

manually checked for errors. We choose to conduct interviews in German as our participants

felt more comfortable with using their mother tongue and we expected to get a more detailed

description of their thoughts and opinions about their experience of using lean startup for digital

BMI in established companies.


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3.4 Data Analysis

Aligned with our methodology, we use the open approach of grounded analysis to analyze our

gathered data. Grounded analysis is a holistic approach in qualitative research, often used in

combination with grounded theory methodology, aiming to develop a theory based on given

data (Easterby-Smith et al., 2018). We see this technique as suitable for our study as it gives us

the possibility to not only confirm existing theory on lean startup and digital BMI but also to

discover new findings to get deeper understanding of the research phenomena. As we are using

an abductive approach and aim to identify enablers and barriers of lean startup as a tool for

digital BMI, grounded analysis offers us to explore novel insights about the research topic,

beyond the usage in digital startups, in the context of established companies. Therefore, we

intend to compare different perspectives of our research participants and their experience of

using lean startup in established companies to derive new knowledge. Thus, we aim to

contribute and extend existing literature about lean startup and digital BMI from using it in

startups to the use in established companies. Through constant comparison of the data,

following the seven steps of grounded analysis, we identified common patterns in form of codes

and categories and built a holistic understanding and theory of the studied phenomenon. The

seven steps are (1) Familiarization, (2) Reflection, (3) Open or initial coding, (4)

Conceptualization, (5) Focused re-coding, (6) Linking, and (7) Re-evaluation (Easterby-Smith

et al., 2018).

Following these principles, we first familiarized ourselves with the data by listening again to

interview recordings and going through interview transcripts. We already started with

familiarization in the data collection process by writing notes during the interviews, listening

to our recordings, and reading interview transcripts several times. We made initial comments

and frequently discussed our impressions and thoughts after each interview. Whenever we

identified interesting new topics related to lean startup as a tool for digital BMI, we adjusted

our interview guide for the next interviews. These adjustments are illustrated in our interview

guide (appendix 1) as optional questions emerging from the interviews. After conducting all

interviews, we reflected on the data in the context of our study asking what enablers and barriers

occur when using lean startup for digital BMI in established companies. Afterward, we went

through our interview transcripts for initial coding once more, by summarizing the most

important quotes from our interviewees in codes. For the first coding, we worked together

taking the first data set (Respondent 1) to identify and discuss a first set of codes that seemed


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to be important, before we continued individually for the further coding process. The coding

has been conducted using ATLAS.ti, which allowed us to follow each other’s coding process.

Even though we worked independently to increase the quality of our analysis and decrease bias,

we constantly updated each other about the process and findings. We used the initial codes as

guidance and added new codes throughout the process, which we then discussed to share the

same understanding of the data. After the individual coding, we came together to debate about

all emerged codes, their importance, characteristics, and uniqueness. We identified 176 codes

in our initial coding, which we grouped into 18 initial categories by comparing codes,

identifying similarities, and patterns of the data regarding lean startup for digital BMI in

established companies. Based on that, we went through our transcripts again to recode the data

in a more focused manner, renaming, restructuring, and finalizing our codes. That way, we

deepened our analysis and worked even closer with the data focusing on the most important

facts in the context of our research question. We ended up with 79 codes, structured into 16

pre-categories. Further, we conceptualized our findings into six main categories, namely (1)

lean startup management, (2) organizational structures, (3) culture, (4) corporate governance,

(5) methodical competence, and (6) external influences. Thus, we are now referring to the initial

16 pre-defined categories as sub-categories that helped us to group our final categories. From

the 16 pre-categories, we transferred two (organizational structures and external influences)

directly into main categories, as they could not be combined with any other sub-category. Thus,

the final coding shows 79 codes, 14 sub-categories, and 6 categories (appendix 3). Following

the principles of grounded analysis, we discussed interrelations of our categories by identifying

linkages that have been transferred in a framework showing enablers and barriers for using lean

startup as a tool for digital BMI in established companies. As the last step, we re-evaluated our

analysis process by going through our data, codes, and categories again to confirm our findings

and to answer our research question. Figure 3 shows our final coding tree displaying example

codes for each sub-category/category. Further, we will provide example quotes for these codes

later in our empirical data (chapter 4, table 2).


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Figure 3: Final coding tree with example codes.


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3.5 Research Ethics and Quality Insurance

Ethical considerations are important to respect in research and to ensure that no harm occurs

(Easterby-Smith et al., 2018). We ensure ethical correct research by following the key principles

by Bell and Bryman (2007). In the following we are outlining how we applied these principles

throughout our research, and further highlight how we ensure quality of our study:

(1) Harm to participants – We ensure physical and psychological well-being of our participants

to avoid any harm, by creating a safe and comfortable research environment. As we selected

respondents via online research without having any personal connection to them, we offered to

have a prior phone call, where we presented ourselves, and our study’s intent as well as

boundary conditions of the interview. This was important to gain trust and to create a safe and

comfortable environment for all respondents. Moreover, we did not ask any inappropriate

questions about potentially sensitive topics, such as too personal data, company details, or

financial aspects, and all participants were able to refuse to answer at any time. This is

especially important for our study, as some respondents act as external coaches that underlie

strict corporate non-disclosure agreements. This way, we avoid that respondents felt

uncomfortable as well as incorrect, biased answers (Saunders et al., 2012).

(2) Dignity – We respected our research participants’ dignity regarding all answers and opinions

without influencing or guiding them towards a certain direction. We ensure that by the design

of our interview guide with only asking open and non-judgmental questions. This was

especially important regarding our research topic, as we investigate lean startup as a tool for

digital BMI in established companies from multiple perspectives by including internal

perspectives of employees as well as external perspectives of consultants. Before we conducted

the interviews, a test interview was set up (see chapter 3.3.3) to ensure that all questions are

asked appropriately. With open questions, we intended to not personally bias respondents as

well as to motivate them to elaborate on further details to uncovered parts within the interview

guide. Thus, we added optional questions to the interview guide (appendix 1) throughout the

interview process whenever respondents highlighted new insights on lean startup for digital

BMI that has not been covered by leading questions before.

(3) Informed consent – We ensured fully informed consent of our research participants by

sending out a GDPR consent form (appendix 2) before the interviews. By doing so, an e-mail

was sent to all respondents where we asked for their willingness to participate in our research


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and informed them about their rights concerning privacy, anonymity, and confidentiality

according to GDPR. In addition to the written agreement, we asked again for their verbal

consent for participation as well as the recording of the interview at the beginning of each

interview. Only after the verbal consent, we started recording the interview as well as taking

notes.

(4) Privacy – To protect our research participants’ privacy we do not process any personal- or

company-related data that could be linked to any interview partners. As many respondents are

consultants working with lean startup in various companies, it is important for us to also protect

and respect privacy of our research participants’ customers. Coherently, all information is only

processed according to GDPR which all participants have been informed of before the

interview. Thus, we are not processing information related to our interview partners to any third

party.

(5) Confidentiality – All recordings and interview transcripts are securely stored and only

accessible by the two of us for the purpose of this study. Thus, we ensure confidentiality of our

research data and do not hand any confidential information to third parties. All data will be

deleted when it is not needed anymore for this study.

(6) Anonymity – We protect the anonymity of our research participant, their employers, and

business partners. Thus, we are not stating any names within this study and refer to the

interviewees as “Respondent X”. Further, all confidential information in our interview

transcripts has been blacked to ensure that no personal- or company-related data is being

processed. Again, this is specifically relevant for our study as some research participants

function as external consultants for well-known established companies, where they are

underlying strict corporate non-disclosure agreements.

(7) Deception – To avoid any deception about the nature or aim of the research we transparently

outline the research procedure by illustrating the purpose of our research and all decisions that

have been taking accordingly. Thus, we also aim to increase dependability of our study,

meaning the results of our study are not influenced by the chosen method and a stepwise

replication of the study is possible (Guba, 1981). Even though qualitative research is difficult

to replicate, we argue that we give a transparent and systematic overview of the study procedure

in terms of data collection and data analysis which is comprehensible by the audience.


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Additionally, we achieved data saturation with our ten interviews as no new insights about lean

startup as a tool for digital BMI in established companies emerged after the 9th interview, and

interview 10 rather confirmed and deepened the already gained knowledge about the topic.

Thus, we do not expect additional interview partners to reveal new insights about lean startup

and digital BMI in the context of established companies. Further, as we used grounded analysis,

we let knowledge about enablers and barriers only emerge from data. We only used existing

literature to familiarize with the topics of lean startup and digital BMI to better understand the

studied phenomenon when analyzing the data, but not taking pre-assumptions into account.

Additionally, the research has been conducted by the two of us, first analyzing the data together

to get a common understanding, then continuing independently, and later re-evaluated by

comparing our results and cross-checking the data together.

(8) Affiliation – In the case of our research, no affiliation applies as we chose the topic ourselves.

Thus, the research purpose has not been assigned to us. Furthermore, there is no conflict of

interest as we did not receive any funding. The only affiliation of our study is the connection to

Jönköping University, which has been stated in our GDPR consent form. However, we do not

expect any influence or bias by mentioning Jönköping University, as we did not acknowledge

any negative affiliations of our interview partners with Jönköping University.

(9) Honesty and transparency – We ensure honest and transparent communication about our

study throughout the whole research process. This applies first in terms of our research

participants regarding the processed data, as we clearly stated the study’s intent as well as how

we are using their data. Thus, we created a trustful connection with our research participants.

Furthermore, we aim to create trust for all researchers and managers interesting in our research,

by fully outlining our research process and decisions taken within this study. Therefore, we

ensure credibility of our data by displaying and interpreting our results according to the

participants’ perspectives (Guba, 1981). Even though interviews per se are a limitation to

research, as interview partners cannot give full access to information, we compared multiple

perspectives to identify common patterns and similarities. By including employees, innovation

incubators as well as consultants using lean startup as a tool for digital BMI, we combine

internal and external perspectives about our studied phenomenon in the context of established

companies. Hence, we identified similarities and differences of perspectives and thus avoid bias

(Easterby-Smith et al., 2018). Further, credibility is ensured by using triangulation while

analyzing the data as we worked independently and controlled each other to decrease


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subjectivity of our findings. Therefore, we met during the analysis process to first initially code,

to group our codes, to re-code the transcripts, to conceptualize our findings, and identify

linkages between categories, and finally to re-evaluate our results.

(10) Reciprocity – With our research, we aim for mutual benefit for us as researchers, for other

researchers with our contribution to existing theory, for managers/companies working with or

aiming to work with lean startup as a tool for digital BMI, but also for our research participants.

As we highly depended on our interviewees’ experience and their insights regarding lean startup

and digital BMI, we want to acknowledge this by sharing our results with them in return. Even

though transferability of qualitative research is difficult given its unique research environment,

taking all limitations such as sampling, interview partners, industries, context, etc. into account

(Guba, 1981) we aim for internal generalizability as we can only explain and generalize our

findings within the given research setting (Easterby-Smith et al., 2018). Thus, we can only argue

that our findings apply to the specific setup of our research. This might implicate a lack of

transferability of our research. However, we argue that with our sampling of diverse interview

partners from different industries, roles, and different experiences, we already gained a holistic

view and detailed insights about the use of lean startup as a tool for digital BMI in established

companies which can be, to some extent, generalized. Hence, such theoretical purposive

sampling increases transferability of our study (Guba, 1981) and thus is valuable for

researchers, managers/companies, and our research participants.

(11) Misrepresentation - To protect integrity of our research and to avoid any misleading or

false reporting, we ensure accuracy and transparency of both our research procedure and our

research findings. Further, we avoid bias in our study, as we conducted the research as a pair

and controlled each other throughout the process according to the outlined principles. This also

aligns with the criteria of confirmability, as the research is not influenced by researchers

themselves (Guba, 1981). Even though we let data occur from interactions with our interview

partners according to Charmaz (2000), we are reflective observers and therefore objective

within the research environment. Thus, results are based on our research participants’

experience and not guided or manipulated by us as researchers. Even though bias can emerge

from individual perspectives of interviewees in semi-structured interviews (Saunders et al.,

2012), we ensured confirmability of our findings by comparing similarities and differences of

the interviewees’ answers to get a holistic understanding of enablers and barriers of lean startup

as a tool for digital BMI. Also, as mentioned, we used triangulation to minimize subjectivity


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and worked individually as well as together to analyze and compare our findings to check

reliability of our work.

4. Empirical Findings

___________________________________________________________________________

This chapter outlines main findings of our research by presenting and examining the collected

data. The chapter is structured according to our six categories (4.1 – 4.6) identified in our data

analysis showing enablers and barriers of lean startup as a tool for digital business model

innovation in established companies for each category. _______________________________________________________________________________________________________________________________________________________

While analyzing our empirical data we identified six categories, namely (1) lean startup

management, (2) organizational structures, (3) culture, (4) corporate governance, (5)

methodical competence, and (6) external influences, that help us to answer our research

question:

What are enablers and barriers of lean startup as a tool for digital business model

innovation?

Therefore, we are first examining lean startup management in terms of agility, its connection to

digital tools, handling of hypotheses and MVPs, and customer involvement. We are then

outlining enablers and barriers in terms of organizational structures, culture, corporate

governance, and methodical competence. Finally, we are presenting external factors that

indirectly affect the use of lean startup as a tool for digital BMI in established companies. Table

2 gives insights into each category by outlining representative quotes from our interviewees.


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Categories Sub-Categories Example Codes Example Quotes

Lean Startup

Management

Agility

Digital Tools

Handling of Hypotheses

Handling of MVP

Customer Involvement

Generating learnings

Resource-efficiency

Open-ended process

Importance of digital tools

Facilitator

Scaling potential

Creating hypotheses

Testing hypotheses

Validating hypotheses

Quality of MVP

Identifying core element

Forms of MVP

Customer centricity

Customer feedback

Customer distance

“It is important to consider that you can fail, but you work fast, and you also learn fast. There is always something

that you take out of the process.”

“For us it also means to work resource-efficient, money and time wise.”

“Your starting point is not a solution it is a problem you want to solve and then you look what could be the right

solution to solve it.”

“Digital tools play a huge role as they make you faster.”

“(…) they facilitate the process.”

“Digital tools offer you scaling potentials.”

“Hypotheses are always somehow created with the customer.”

“Lean startup is not about generating ideas, but to test if an idea works for the user or not.”

“I think it is important that you validate the right hypotheses. That you validate the hypotheses you can validate.”

“Companies are afraid of ‘delivering qualitative crap’, simply because it’s not being perceived as this MVP, it’s

being perceived as a product that someone is releasing.”

“It is important to identify and narrow down to the core element.”

“Something physical can be beneficial at some point. But digital MVPs can be adjusted easily and faster.”

“You really need to engage with the customer and have in mind that it is not about a function or solution, it is

about an underlying need.”

“You get the BM smashed very early and very crudely from a different perspective.”

“In big companies it is often the case that employees do not know who the customers are neither what they want.

They are too far away from the customers.”

Organizational

Structures

Existing organizational structures

Changing procedures

Flat hierarchies

“It is difficult to implement lean startup in established processes as those are always prior to new processes and

ideas.”

“The good thing with lean startup is that you can also transform internal processes once the method is

internalized.”

“Successfully working with lean startup requires flat hierarchies and a dynamic environment.”

Culture Corporate Culture

Mindset

Cultural change

Error culture

Openness

“Doer” mentality

Persistence

Willingness to experiment

“If lean startup is well received, if it is accepted, if that commitment is there, I think it can be very beneficial for

cultural change.”

“(…) a healthy error culture. So being willing to say I learn from what I do, saying that the process is the way.”

“A traditional company needs to be open to new opportunities.”

“As a prerequisite, a traditional company needs a doer mentality, or at least from the people or team that has to

deal with it.”

“That you have to be patient and that you have to be open-minded (…). That also has a lot to do with persistence.”

“Willingness to experiment and to open up towards the customer (…).”


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Personal Culture

Motivation

Courage

Fear

Small degree of arrogance

Employees driving change

Intrinsic motivation

Volunteers

“Sometimes the courage to go that way is missing.”

“Often it is the fear of being replaced and not to be needed anymore.”

“And with that comes a very, very small degree of arrogance.”

“(…) they actually realize, even though it may be a small thing, but they realize ok what I’m doing is having an

impact in some way.”

“And that’s where intrinsic motivation is necessary.”

“So volunteering is also important, that people are not forced to do it, but that they are willing to do it.”

Corporate

Governance

Management

Leadership

Commitment

Lack of foresight

Loose of power

Freedom and independence

Open and transparent communication

Creating save environment

“And there must also be a commitment from the company and the management to be successful in the long term.”

“(…) most companies simply lack the foresight for lean startup.”

“Because all agile methods come with a certain loss of power for the manager level.”

“(…) but there simply has to be an atmosphere where everyone feels free and actually is free and also has the

time.”

“You need to be as open and transparent as possible in the process.”

“So, you somehow need to provide a protected space.”

Methodical

Competence

Knowledge

Training/Education

Teaming

Knowledge transfer

Practical experience

Theoretical knowledge

Multiplicators

External coaches

Education of employees

Mixed teams

Diverse capacities and expertise

Interdisciplinarity

“You need to provide training and knowledge to employees and guide them in the process.”

“Above all, I think it’s important to know how to put it into practice.”

“And you read about it in many places.”

“(…) and you need multiplicators.”

“I think you need coaches; I mean you need people who can teach you how to do that.”

“(…) and then train, classically, first show, teach, and explain.”

“I think you benefit a lot from having a mixed team with some experienced people who know the established

processes, but also people who are completely new and fresh to the problem.”

“With different skills, which means you also need a doer mentality, on the one hand, within the team, but you also

need doing abilities.”

“That really means interdisciplinarity is important.”

External

Influences

Role of competition

Role of governance rules

Changing market conditions

“It is hard for large companies to compete with startups, as they are faster and more agile. (…) They think they

could copy startup ideas fast, but they can’t.”

“(…) but it also required a certain governance framework as to how innovation is systematically anchored in a

company.”

“I think that it’s also a kind of prerequisite for anything to happen on the outside that puts the company in turmoil

or puts pressure on it to change.”

Table 2: Overview of categories and sub-categories with example codes and quotes.


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4.1 Lean Startup Management

The fast implementation of and experimentation with ideas are highlighted by the respondents

as major advantages of using lean startup for digital BMI. Looking at the core of lean startup,

we identified agility, digital tools, handling of hypotheses and MVPs, as well as customer

involvement as essential factors for successful usage of lean startup for digital BMI in

established companies.

4.1.1 Agility

Agility refers to the intuitive, iterative, open-ended, explorative, and resource-efficient nature

of lean startup. Furthermore, generation of learnings, risk management, discarding ideas, and

the combination with other methods are named by the respondents regarding agility of lean

startup.

Most respondents notice how companies and employees are often surprised by “what is

possible in a very short time” (Respondent 8) when using lean startup for digital BMI, as they

are not used to auch agile way of working. Respondent 9 even calls it “structured chaos”

enabling established companies to think out of the box and be innovative. According to the

respondents, lean startup is often used in cases where there is no or little time and money

(Respondents 1;2;5;6;8;10). They describe it as a very resource-efficient method to minimize

risk when developing new BMs. Further, the respondents highlight the importance of learning

processes from lean startup as “experiments can never turn out negative as they always gather

results” (Respondent 2). It is the experimental character that is core of lean startup.

Accordingly, Respondent 7 explains that lean startup is about “starting with a problem rather

than a solution, and then looking which solution fits the problem best”. However, Respondent

8 argues that such open-ended process can raise uncertainty for established companies as they

do not know where the process leads them. In addition to that, expectations can also be a

problem as often “people perceive it as not really finished” (Respondent 6). Further, the

respondents explain that build-measure-learn iterations can be challenging and that it is difficult

to define an appropriate time to stop iterations (Respondent 5). Another important aspect of

agility in the lean startup process is discarding ideas, which can be a huge challenge

(Respondents 5;6;10). The respondents argue that individuals can be too convinced by own

ideas and not putting customers’ pain in focus anymore. Thus, it can hinder usage of lean startup

for digital BMI. Moreover, lean startup is not a stand-alone method as the respondents point

out that it is often combined with other methods, such as Design Thinking. Respondent 1


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mentions “lean startup is not about generating ideas but to test if an idea works for the users.”

Hence, Design Thinking seems to be a potential pre-step before starting with lean startup.

4.1.2 Digital Tools

Our empirical findings highlight a general importance of digital tools within the lean startup

process and further, the respondents argue that these are a crucial facilitator and offer scaling

potential.

The respondents see digital tools as facilitators and enablers of lean startup, as they are not only

making BMI but also the lean startup process itself faster and easier. Even though Respondent

1 states that lean startup can be used without digital tools, advantages prevail. Especially, in

terms of prototyping, building MVPs, and collecting customer feedback they play an important

role. As Respondent 2 puts it, being fast is most important in lean startup and digital tools

contribute to this matter. This also goes along with saving time. Hence, digital tools facilitate a

resource-efficient way of working (Respondent 6). Especially, for established companies they

offer new, diverse possibilities to interact on large scale, internally as well as externally

(Respondent 10). Respondent 7 adds “digital tools are important, as they facilitate user

feedback and input”. Hence, they are facilitating testing and validating of hypotheses and

enable experimentation (Respondent 10). Therefore, digital tools enable lean startup for digital

BMI, as they facilitate the lean startup process, by making it faster, more efficient, and easier

to work internally as well as collaborate with customers.

4.1.3 Handling of Hypotheses

Hypotheses are an important element of lean startup. Our research revealed that right handling

of hypotheses in terms of creating, testing, and validating hypotheses is essential to successfully

work with lean startup as a tool for digital BMI.

Handling hypotheses to make formulations for each BM component is a difficult task for

established companies (Respondents 1;4;5;7). On one hand, the respondents argue that

formulation of hypotheses per se is hard and time-consuming and needs guidance, either from

consultants/coaches or colleagues and management. On the other hand, it is hard to fill in BM

components with pure assumptions (Respondents 1&4). The respondents commonly agree that

it is difficult to create right hypotheses but that this is crucial for testing. Often it helps when

hypotheses are already created together with customers (Respondent 7). A major challenge is


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to listen to customer feedback and to not manipulate data to get preferred outcomes (Respondent

5). According to Respondent 4, it all “begins with formulation of a good hypothesis. And

generating good hypotheses requires good observation skills and a certain accuracy for

details.” Once hypotheses are created, validation through MVPs helps getting quick feedback

from customers to edge hypotheses and to set the right focus (Respondent 7). Thus, handling

hypotheses can be an enabler or barrier of lean startup for digital BMI, and it is important to

create good hypotheses that can be tested and validated to facilitate the lean startup process and

to make right assumptions about BMs.

4.1.4 Handling of Minimum Viable Product

Another main element of the lean startup process is the MVP. Similar to hypotheses, also

handling MVPs right is crucial to successfully work with lean startup for digital BMI. In this

context, role of MVPs, building MVPs, as well as forms and quality of MVPs have been

identified as enabling and hindering factors of lean startup. Furthermore, the respondents

highlight the importance of identifying an MVP’s core element(s).

As mentioned before, the respondents agree that lean startup is used as a tool for digital BMI

with comparatively low initial costs when building an MVP and, thus, requires to work with

only little resources. Therefore, it is important to identify the core element of a MVP and reduce

it to the actual minimum (Respondent 6). According to Respondent 1, it is a useful tool with

which companies can quickly indicate if a BM is feasible or not. The respondents summarize it

as a fast way of testing and validating hypotheses with low expenses to get fast feedback. Thus,

it offers a short time to market (Respondent 10). Still, handling of MVPs is difficult for

established companies as they associate building an MVP with building a product of minor

quality (Respondent 1). Respondent 10 explains that with a “missing courage of delivering

‘unfinished’ products” and Respondent 6 therefore suggests publishing MVPs under a different

name than the corporate brand. According to Respondent 8, employees can be overwhelmed in

the beginning as they do not know where to start and what to build. Therefore, the respondents

argue that it is easier to build an MVP starting with a “click and feel dummy” (Respondent 6)

to make it look real for customers (Respondent 10). In this context, the respondents also

highlight the importance of digital tools as they make the development process and collection

of customer feedback faster and MVPs easily adjustable. Hence, the respondents argue that

testing without digital tools is almost impossible nowadays. The right handling of MVPs

becomes crucial for the lean startup process as without a suitable MVP, hypotheses about a BM


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cannot be validated correctly and, thus, hinder usage of lean startup for digital BMI. In turn, a

good MVP facilitates validation of hypotheses and offers established companies a short time to

market for their digital BMs.

4.1.5 Customer Involvement

As mentioned before, lean startup is a customer-centric approach and thus, involvement of

customers becomes important for successfully working with lean startup in established

companies. Therefore, we identify customer centricity, distance, and feedback as enablers and

barriers of lean startup for digital BMI.

Customer-centricity is a major part of lean startup to identify customer pain, to create

hypotheses, and to test hypotheses with MVPs. The respondents highlight the importance of

early customer engagement and multiple involvements throughout the process. To get high-

quality feedback, it is important to identify suitable group(s) of customers (Respondent 8) and

to have a good MVP that helps to validate hypotheses (Respondent 4). Customer involvement

then eases to evaluate BMs from a different perspective, as it gives “completely new impulses,

new ideas, or draws your attention to problems that you wouldn’t have seen yourself”

(Respondent 8). By engaging with customers, companies can discover underlying needs instead

of delivering features and functions (Respondent 4). Primary goal should be to satisfy those

needs (Respondent 5). In established companies, customer distance can also be a challenge. As

pointed out by the respondents, the bigger a company is the farther away are employees from

customers, meaning they do not know who the customers are neither what they want.

Respondent 4 even adds that “in some companies it is forbidden for employees […] to talk to

the customer”. Furthermore, some companies are afraid of customer contact and potentially

negative feedback emerging from it (Respondent 8). Nevertheless, lean startup helps to

understand customers better, to indicate their needs (Respondent 2) and supports companies to

build a future customer base for novel digital BMs (Respondent 1). Therefore, it is important

to listen to customers, to identify and include suitable groups of customers, and to close the gap

between companies/employees and their customers. Thus, customer involvement can be a huge

enabler when using lean startup for digital BMI.

In terms of lean startup management, resource efficiency enables usage of lean startup, as

companies tend to use the tool based on this characteristic. However, the open-ended process

can impede the lean startup process, as individuals are unfamiliar with this novel way of


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working and might be overwhelmed. Therefore, it forms a barrier. Same accounts for handling

of MVPs and hypotheses, as established companies are not used to work with these techniques

and might refuse to release a product of minor quality. Opposingly, customer centricity forms

an enabler for lean startup and digital BMI, as established companies can overcome customer

distance with it. In all this, digital tools play a facilitating role, enabling usage of lean startup

for digital BMI in established companies.

4.2 Organizational Structures

Organizational structures form a crucial element when using lean startup for digital BMI. In

this context, existing organizational structures, changing these structures and procedures, flat

hierarchies, and separation of lean startup projects emerged as enablers and barriers for lean

startup as a tool for digital BMI in established companies.

The respondents commonly agree that it is impossible to implement lean startup without

changing organizational structures. Existing organizational structures are pointed out to be both

enabling and hindering in this context (Respondent 10). On one hand, lean startup can benefit

from resources of a company (Respondent 2) and existing structures such as “distribution

channels, customer networks, technologies, etc.” (Respondent 8). On the other hand,

established processes, stiff structures, high bureaucracy, and long ways of communication are

named by the respondents to inhibit the usage of lean startup for digital BMI. Even though the

respondents say that existing structures could facilitate the usage of lean startup, some perceive

it generally more as a barrier in established companies. Established companies often developed

specific business processes over the years, and thus get stuck in their routines. When

implementing lean startup to become faster and more agile, Respondent 2 mentions that

companies struggle to change organizational structures accordingly. As per Respondent 4,

“every company that is fundamentally hierarchical organized should be reorganized and

restructured from scratch.” Without undertaking changes in organizational structures, “old and

new way of working will automatically clash” (Respondent 2). However, established companies

have difficulties breaking up existing structures and hierarchies. The bigger a company, the

more complex its structures and the harder it is to change, and in turn the smaller a company

the faster lean startup can be adopted (Respondent 6). Respondent 8 further highlights, that

existing structures and procedures will most likely be prioritized over new, agile projects.

Therefore, the role of existing structures becomes more important the closer it is to a company’s


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core business (Respondent 8) and builds a barrier for using lean startup as a tool for digital BMI

in established companies.

According to Respondent 7, established companies “are now encountering more and more

difficulties and are simply realizing that they have to develop new things in a much shorter time

and be flexible and creative in their way of working and thinking.” Thus, using lean startup for

digital BMI does also require changes in procedures, but it could take a few years for established

companies (Respondent 6). Moreover, it requires organizational change on a large scale to have

an impact on companies, as otherwise it is most likely that they return to old routines

(Respondent 9). However, as a minimum, existing organizational structures, especially very

hierarchical ones, need to be removed for team(s) directly working with lean startup to be

flexible (Respondent 7). This also implies a separation of lean startup projects from companies’

core businesses. When asking the respondents if and how lean startup projects/teams need to

be separated from the core business, we received controversial answers. On one hand, a

separation can reduce potential conflicts (Respondent 1) and on the other hand, it can convey a

certain statement towards the rest of the organization and thus, can be perceived negatively

(Respondent 4). Therefore, the respondents argue that it is important to openly communicate

the aim, how it will impact the entire organization and that it has no danger to existing business

(Respondent 1&3). When separated from core businesses, lean startup empowers to work more

flexibly and creatively and to think outside the box (Respondent 5), but there is also risk of

losing connection to the core business (Respondent 3). According to Respondent 7, separation

of lean startup projects can be especially helpful in the beginning of innovation processes as

these teams dependent less on the core business and therefore, are more likely to take risks and

experiment. Even though a physical separation of lean startup projects can be helpful to be more

disruptive outside existing structures (Respondent 9), it is important to find a way to establish

lean startup in daily business to “do the everyday work while experimenting with new and

radical things” (Respondent 10). Therefore, it is key to create independence for new, lean

projects while simultaneously being connected to core business (Respondent 10). “That is the

keyword of ambidexterity”, Respondent 8 states, “to adopt a two-pronged strategy to let the

new and existing one co-exist, to communicate and let them facilitate each other.” Therefore,

it requires open and transparent communication. Furthermore, flat hierarchies are a main

requirement for using lean startup in digital BMI (Respondents 6&9), along with restructuring

of people and responsibilities (Respondent 6), and interdisciplinarity of teams (Respondent 7).


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In terms of organizational structures, hierarchical levels, long ways of communication as well

as high bureaucracy slow down the lean startup process. In addition, when existing structures

and procedures are prioritized over new agile projects, they form barriers for the successful

usage of lean startup for digital BMI in established companies. In contrast, existing resources

of established companies can be an enabler for lean startup. By adapting a two-sided strategy

and implementing flat hierarchies, established companies can foster an agile environment for

lean startup while maintaining existing businesses.

4.3 Culture

We also discover culture playing a huge role when using lean startup in established companies.

More specifically, corporate culture, corporate and personal mindset, personal culture of

individuals within the company, as well as their motivation are influential factors hampering or

facilitating usage of lean startup for digital BMI.

4.3.1 Corporate Culture

Several respondents agree that corporate culture has a high influence on using lean startup in

established companies (Respondent 1;2;3;4;8;9) in terms of being open to make mistakes, to

culturally change, and to drive agility.

Respondent 1 explains that existing culture is crucial to successfully use lean startup for digital

BMI. One often-named factor for successful integration of lean startup is to have an open error

culture within established companies (Respondent 1;2;4;7;8;10). Some respondents (1;2;4)

explain that established companies try to avoid failure and risk and are taught to an error-free

execution of their work. Individuals within organizations are therefore too afraid to make

mistakes and to experiment: “I don’t want to do a mistake, cause I’m afraid of being punished

for it” (Respondent 1). Thus, several respondents see an urgent need for an open error culture

in established firms, as without “there will be no culture of trying, experimenting, and

learning” (Respondent 2). Hence, it is important to create awareness that systematic learning

processes are more valuable for digital BMI than a zero-error attitude (Respondent 4). Also, it

is crucial to build psychological safety, where it is deliberately chosen and supported to make

mistakes, to fail fast, and to learn quickly (Respondent 8). However, in established companies

this is a difficult undertaking, as existing values and ways of working are already established

and internalized for a long time (Respondent 4). Moreover, it is hard for established companies

to differentiate in which business area to apply a zero-error culture and where they allow to test


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and experiment with novel approaches, as well as to manage this separation (Respondent 10).

Also, openness towards agile methods of established companies is mentioned by some

respondents (1&8). Respondent 1 explains that this is crucial because, without openness to

innovate, to give room for experimentation and try novel solutions, established companies

cannot work with lean startup for digital BMI in first place. To use lean startup successfully in

established companies, several respondents (1;4;5;6;7;8;9) argue that it needs a cultural change.

On one hand, some respondents argue that working with lean startup automatically brings

cultural change in established companies (Respondents 4;5;6;8). On the other hand, there can

be a strong resistance towards cultural change, as many established companies are too rigid and

do not want to change their ways of working (Respondents 7&8). Respondent 9 even states that

working with lean startup has only little effect on cultural change, because established corporate

culture is just too strong. Hence, first culture and mindset of the people need to change to a

more agile culture, to successfully work with lean startup for digital BMI (Respondent 9).

4.3.2 Mindset

Also, corporate and personal mindset about being willing to change traditional mindset, to

experiment, and to learn new things, being persistent, as well as a “doer” mentality, are essential

when working with lean startup for digital BMI.

Most respondents (1;2;3;4;5;7;9) argue that individuals in established companies are oftentimes

trapped in traditional mindsets, which have been built up for a long time. Therefore, they are

less willing to change existing ways of business activities (Respondent 7) and are caught in

their traditional thinking and role, as individuals need to deliver results in the corporate

environment to secure their jobs (Respondent 4). Respondent 9 adds that it is always a challenge

for people working in established companies to get away from efficiency mindset and to stop

thinking about generating money. Thus, several respondents request a change of mindset in

established companies to work with lean startup and to survive in the novel, fast-paced, digital

environment (Respondent 3;4;6;7;8). Hence, established companies need an agile mentality, as

several respondents (1;2;7;8;10) express it as a “doer mentality”. In addition, individuals need

to overcome their skepticism for lean startup. Some respondents experience that unfamiliar

tools, such as lean startup, are hard to be accepted by individuals within established companies,

as they feel “uncomfortable with the unknown” (Respondent 2). Thus, willingness to learn,

experiment and change established ways of working is essential for successful use of lean

startup for digital BMI (Respondent 1;7;8;9).


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4.3.3 Personal Culture

Further personal culture in terms of courage and being able to overcome fear and arrogance are

important when using lean startup in established companies.

Respondent 1 argues that individuals need to be brave, try out new things without planning too

long and just start acting. Yet, Respondents 4 and 7 doubt that individuals in established

companies can overcome their fear, as “they don't have guts to go down that path” (Respondent

4). Respondent 7 sees one of the main problems in people who have strong ambitions for a

career, as they are not interested to change the known and want to stay in their established

position. By using lean startup and innovating existing BMs to digital BMs, they fear to lose

their positioning within the company (Respondent 7). Respondent 3 adds that oftentimes the

own ego stands in the way, disturbing digital BMI processes. Hence, Respondent 1 argues that

for successful usage of lean startup for digital BMI, people need to overcome their arrogance

and start to accept that they do not have ultimate knowledge about customer needs. In turn, a

small degree of arrogance facilitates the lean startup process.

4.3.4 Motivation

Another crucial factor of culture is motivation of individuals within established companies to

work with lean startup. Thus, volunteers and intrinsic motivation are playing a big part for

successfully using lean startup for digital BMI, as well as employees need to be willing and

able to drive change.

In relation to this, several respondents argue that motivation to work with lean startup for digital

BMI needs to come intrinsically (Respondent 1;8;9) and should not be enforced by the

management (Respondent 2). Thus, it requires volunteers that are willing to work and

experiment with lean startup and that are enthusiastic about such tools (Respondents 2;6;9)

which will in turn lead to more creativity in the iterative process (Respondent 1). To foster

motivation, employees need to experience that they can be effective themselves and induce

something within the company and that “they are not just a small part of a big gear”

(Respondent 2).

In terms of culture, traditional mindset of individuals and corporate hinders cultural change

towards more agility and thus, needs to be overcome. Established positioning and

accompanying high degree of arrogance also form a barrier when using lean startup for digital


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BMI. In addition, missing persistence hinders long-term success of lean startup. Opposingly,

high willingness to change, experiment, and learn as well as an error culture encourages

business experimentation which is beneficial for lean startup as a tool for digital BMI. Also, a

doer-mentality and intrinsic motivation of employees is building an enabling factor for using

lean startup.

4.4 Corporate Governance

Another influential factor emerging from our empirical findings, either enabling or inhibiting

the usage of lean startup for digital BMI, is corporate governance. In this context, we distinguish

between management and its role in the lean startup process, as well as their leadership style.

4.4.1 Management

Most respondents (2;4;6;7;9;10) agree that the role of the management is highly important for

successfully working with lean startup in established companies to operate with digital BMI.

Specifically, attitude and commitment towards lean startup and digital BMI, their lack of vision,

foresight, and education as well as fear to lose power.

According to the respondents, management can influence lean startup processes either

positively or negatively. Therefore, respondents argue that it is important to have management’s

commitment and support during the process, as managers are decision-makers who are

responsible for everything, including usage of lean startup (Respondent 6). So, to have

management on board is crucial “for legitimacy” to work with lean startup as it is “often

diametrically opposed to other ways of working in organizations and thus somehow also stands

out” (Respondent 10). In this context, it is argued that it requires the management to drive

change in companies as “a bottom-up approach […] leads to limits” at some point “if the upper

level is not on board” (Respondent 2). Respondent 7 argues that managers also have to set an

example to create the right atmosphere for all individuals to adopt to the new, agile ways of

working. Respondent 4 even sees the management as “bottleneck”, impeding the successful

usage of lean startup for digital BMI. Opposingly, the role of management is perceived by other

respondents as being less important when using lean startup for digital BMI, as they focus more

on process’ outcome and how it moves the business forward instead of the method itself

(Respondents 2;6;9). Further, the importance of the general attitude by management towards

lean startup for digital BMI occurred from our findings. Some respondents (1;5;8) state that the

management has a positive attitude towards using lean startup, as they know that existing


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structures and ways of working are no longer suitable and too slow to keep up in innovation

processes. Thus, they facilitate and support working with lean startup. Others experienced

managers having mixed feelings and concerns about working with lean startup (Respondent 2).

In some cases, the attitude towards lean startup can even differ among management and

employees, which makes it then challenging to drive companywide change. Although some

board members of established companies see lean startup and digital BMI as a strategic tool to

make their business viable in the future, some others are lacking necessary vision and foresight

(Respondent 4). Often skeptical managers lack knowledge about lean startup, are overwhelmed

by unfamiliar activities, or have a general aversion towards new methods. Therefore, it is

important to make them familiar with the method, as managers will be more likely to accept it

when they see that it actually works (Respondent 2). Another critical aspect in established

companies is that managers generally like to plan things ahead to avoid uncertainty and decrease

risk, as they are responsible for resource planning, and need to approve procedures (Respondent

1). According to Respondent 1, the management is most “afraid of losing control and that it is

not working.” However, there will always be a loss of control for managers to a certain extent

when using lean startup, as “decision-making authority is transferred to people who use it”

(Respondent 5). Therefore, managers need to change their way of thinking by “not asking how

[they] can make [their] employees do that, but rather how [they] can facilitate them in what

they are doing” (Respondent 2). In general, without commitment and dedication of the

management, lean startup projects are more limited and most likely to fail (Respondents

1;2;4;5;7;8;10).

4.4.2 Leadership

The second part of corporate governance refers to leadership style of the management in

established companies that fosters or impedes usage of lean startup for digital BMI. In this area

we discover that creating a safe environment, freedom and independence, open and transparent

communication, setting the right boundary conditions, giving guidance, motivation of

employees, equality, and trust are influencing factors.

Freedom and independence are two of the most important factors enabling to innovate digital

BMs with lean startup (Respondents 1;2;3;4;5;7;10). Freedom is important not only in creating

a setting where employees are given more power, and autonomy, but also to think freely aside

from their operational tasks and to get the necessary time besides their core activities


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(Respondent 2;5;7;9). All respondents agree that one of the main strengths of lean startup as a

tool for digital BMI is that innovation processes can be realized in a fast manner with little

resources, and it helps to experiment and get feedback at an early stage. Therefore, people that

work with the method need to have power to decide quickly, otherwise it slows down innovation

processes. Further, it requires managers to trust employees, which is often missing in

established companies (Respondent 2). Thus, open and transparent communication facilitates

usage of lean startup in established companies and can also be beneficial to build trust

(Respondent 1). Nevertheless, employees still need guidance and boundary conditions,

according to the respondents. Respondent 9 believes that individuals cannot be left completely

alone in build-measure-learn processes and always need someone who is familiar with lean

startup and who supports the innovation intend. In relation to that, a frame needs to be set, in

terms of boundary conditions, such as goals, timings, and number of people working on the

project (Respondents 2;5;7;9). Lean startup gives leaders in established companies the chance

to hand over responsibilities to employees, which then leads to a higher level of motivation to

be able to shape and change organizations (Respondent 10). In this context, it is also important

that leaders understand their role as “coach, facilitator, and supporter” (Respondent 1) and

further need to communicate that they are an equal part of the team (Respondent 7). This is

important because the management has direct impact on employees. Thus, leaders support

employees in becoming more independent and to have courage to think and act freely to

experiment with lean startup. If employees feel trusted by managers, they will be motivated,

more confident, and get the possibility to change something within established companies.

All in all, managers need to overcome their high focus on only financial outputs, which form a

barrier for lean startup. Also, a lacking vision and foresight for the future are hindering usage

of lean startup for digital BMI as well as fear of losing control and power. Our findings reveal

that full commitment of the management is necessary to successfully work with lean startup for

digital BMI. Thus, managers need to provide a safe environment, guidance, as well as freedom

and independence for all individuals within the organization, to experiment, to take their own

decisions, and to motivate them, which will then fasten up innovation processes of digital BMs

with lean startup.


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4.5 Methodical Competence

We further identify methodical competence as an influential factor, referring to knowledge,

training and education, as well as teaming, which can both be enabling or hindering usage of

lean startup for digital BMI.

4.5.1 Knowledge

Knowledge in terms of theoretical and practical experience about lean startup, as well as

knowledge transfer, is important to overcome uncertainty and thus are crucial aspects for

successfully working with lean startup for digital BMI.

Respondent 1 argues that “it is important that individuals have knowledge and expertise not

only from books but also from practical experience with lean startup”. Still, in established

companies, there is a lack of knowledge about lean startup as a tool for digital BMI. One reason

is that the method is not practiced in established companies because there is still a big

uncertainty about handling lean startup and its processes right (Respondent 7). This is a

problem, as without experience, deep knowledge and understanding of lean startup, the intent

for digital BMI with lean startup is most likely to fail (Respondent 1). Lack of knowledge

oftentimes ends up in frustration, as Respondent 8 states. To prevent this and to make lean

startup successful, it is essential to provide training and knowledge to employees and

management (Respondent 5). Primarily, it is essential to transfer knowledge about lean startup

to companies. There is a need for gaining deep theoretical knowledge (Respondent 1;5;8) as

well as practical experience and continuous contact with lean startup, as otherwise, lean startup

will not be successful in the long run (Respondents 1;4;7;8;10).

4.5.2 Training and Education

An important aspect linked to gaining knowledge is training and educating employees and

management, which is crucial for the successful use of lean startup. Further, we also refer to

external coaches and multiplicators in this section.

To prevent frustration caused by a lack of knowledge and to use lean startup successfully, it is

essential to provide training and education to employees and management (Respondent 5). This

can be done in workshops and by talking with different departments to explore lean startup and

to gain first understanding of the method (Respondents 5&8). However, transfer of knowledge

serves as a starting point but does not necessarily help to establish usage of lean startup


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(Respondent 4). Thus, it requires to deepen knowledge by educating with different approaches,

such as showing and explaining the method as well as to experience lean startup either in a role

play, in an experiment (Respondent 2), or via different workshops and events, like a method

marathon (Respondent 8). Respondent 1 also adds that established firms should determine

multiplicators of lean startup within firms. These multiplicators serve as lean startup experts

and have the task to spread knowledge across the company and to continuously educate all

employees. Also, persisting education of all individuals within a company has potential to

dismount level of frustration (Respondent 8), as well as uncertainty in handling the method

(Respondent 7). Another common approach mentioned by many respondents is to make use of

external coaches and experts, as they help established companies to experience lean startup

(Respondent 7) and to teach them how to work with the method (Respondent 1&2).

4.5.3 Teaming

Another important aspect mentioned by our respondents is teaming and collaboration of

individuals when using lean startup for digital BMI. The respondents elaborate on the

importance of having small, mixed teams with diverse capabilities and expertise as well as

interdisciplinarity for successfully using lean startup for digital BMI.

Respondent 7 explains that the advantage of mixed teams is that different strengths, different

foci, and inputs are combined. This facilitates the lean startup process for digital BMI as

different viewpoints and dimensions, for instance from marketing, production and sales

perspective are implied. Therefore, to design new BMs some respondents argue that it is

beneficial to have a mixed team of specialists, some that are familiar with established processes,

some that are new and fresh to the topic, as well as specialists of lean startup (Respondent 5&8).

Respondent 8 also recommends mixing teams among generations. Different standpoints of

individuals that are used to traditional values and ways of working, in combination with

generations of freshmen that have not yet internalized traditional company values facilitate

digital BMI with lean startup. Respondent 9 highlights that it is also important to consider

interdisciplinarity on different hierarchical levels, meaning to include managers, trainees,

administrative staff, mechanics, and their foremen. However, teams should not be too big

(Respondent 10). Respondent 10 argues that ideally, a team consists of “maximum five to seven

people”. Small, interdisciplinary teams also help to quickly undergo innovation processes with

lean startup, because otherwise there is a danger to get stuck in the process (Respondent 7).


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Establishing methodical competence throughout the entire organization is necessary as these

build a solid foundation for successful usage of lean startup for digital BMI in established

companies. Hence, established companies need to provide opportunities for their employees to

familiarize themselves with lean startup, both on a theoretical and practical level. Further, lack

of knowledge needs to be overcome by employees and management. From our empirical data,

it becomes apparent that involvement of external coaches and experts of lean startup is

beneficial to overcome lack of knowledge. In this way, the whole organization builds

methodical competence in handling lean startup, which reduces uncertainties, and also fosters

innovation of digital BMs.

4.6 External Influences

In addition to these internal factors, our interviewees elaborate on several external factors that

form opportunities and threats for established companies when using lean startup for digital

BMI. Main factors mentioned by the respondents are changing market conditions, changing

customer behavior, role of competition as well as governance rules.

As for general change of market conditions, Respondent 9 believes that a change in markets

and industries builds a prerequisite to use lean startup for digital BMI. Thus, changing market

conditions cause established companies to change their ways of doing business and triggers a

feeling of restlessness and pressure to react to changing environments quickly. Additionally,

digitalization is named as an important component of why established companies turn to agile

tools, such as lean startup, to innovate BMs, as digital technologies offer new possibilities for

digital BMs. Along with changing market conditions, several respondents (3;7;9) see changing

customer behavior as a vast opportunity for using lean startup in established companies. Thus,

customers have become very picky, because markets offer many diverse options for them to

choose from (Respondent 7). Hence, the challenge for established companies is to predict what

customers need and want (Respondent 2). Therefore, lean startup, with its customer-centric

approach, is a big opportunity to overcome this issue, as respondent 2 adds. Further, role of

competition externally influences a company, as it is hard for established companies to compete

with startups in the digital environment (Respondents 5&7). Often startups operate in a faster,

more agile manner given their organizational structures and have much more power, in terms

of motivation and passion (Respondent 5). Therefore, established companies are using lean

startup for digital BMI to secure a “fighter positioning” (Respondent 7), as with help of such

agile tools they can rapidly change and adapt their BMs by adopting digital technologies, to


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compete with digital startups. Moreover, legal governance rules are hindering established

companies from using lean startup for digital BMI. Several respondents (4;6;7) point out that

for established firms to innovate with agile tools, such as lean startup, there is a need for

“breaking up legal guidelines” (Respondent 6), as with existing governance rules established

firms face many difficulties that inhibit digital BMI processes. As many new digital

technologies enter the market, uncertainties and unresolved responsibilities remain when it

comes to handling private data for instance (Respondent 7). Respondent 4 demands a “certain

governance framework as to how innovation is systematically anchored in a company”, as there

is still no established framework, which in result overwhelms established companies in

innovation processes.

The named external influences are indirectly affecting the use of lean startup for digital BMI.

However, established companies need to be aware of such influential factors to overcome

obstacles and to successfully use lean startup for digital BMI and thus, react fast to changing

market conditions and changing customer behavior.


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5. Analysis

___________________________________________________________________________

In this part we show linkages between outlined categories and answer our research question

by introducing our framework of enabler and barriers of lean startup for digital business model

innovation. Further, we discuss our framework in theoretical context. ______________________________________________________________________________________________________________________________________________________

5.1 Framework of Enablers and Barriers for Lean Startup

Figure 4: Enablers and barriers for using lean startup as a tool for digital BMI.

We argue that digital BMI is an important task for established companies to reinvent their

business, to master digital shift from a purely analog to a digital BM and to stay competitive in

the novel digital environment. We introduce lean startup as an effective method to digitally

transform existing BMs or to innovate new digital BMs in established companies. Emerging

from our empirical findings, we emphasize that successful use of lean startup for digital BMI

is based on effective (1) lean startup management, appropriate (2) organizational structures,

fitting (3) culture, and dedicated (4) corporate governance, which all require solid (5)

methodical competence on all levels of the organization. Furthermore, (6) external influences

such as market conditions, role of competition, or governance rules indirectly affect usage of

lean startup as a tool for digital BMI.


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We introduce our framework (figure 4) displaying how different fields within organizations

contain enabling or hindering factors for using lean startup for digital BMI. It also highlights

interrelations between different influential areas. Methodical competence forms the foundation

for successful usage of lean startup for digital BMI. It is prerequisite for management (e.g.

corporate governance) and all other individuals within the organization (e.g. culture) to

overcome uncertainties and lack of knowledge regarding usage of lean startup. Furthermore, to

create awareness and knowledge of how existing organizational processes, structures,

procedures, units, etc. (organizational structures) organizations can hire external coaches or

determine internal experts or multiplicators of lean startup (methodical competence). If

methodical competence is adopted throughout all areas of the organization, a foundation is built

for the frameworks’ next level – corporate governance, culture, and organizational structure.

Same as methodical competence, these areas each withhold enablers and barriers for usage of

lean startup as a tool for digital BMI in established companies, as stated in the empirical

findings. It is important to overcome the hindering aspects in each area and parallelly focus on

adapting enabling factors. However, the areas are also interrelated, meaning that they influence

each other. Therefore, it is crucial to understand that strategic changes taken in one area can

have a direct influence on the other two areas. For instance, to change hierarchical structures

(organizational structure), it is crucial that management (corporate governance) as well as

employees (culture) are open to proposed changes and can function and work within flat

hierarchical structures. Furthermore, an open error culture (culture) is impossible to implement

if management (corporate governance) does not provide a safe space to experiment and trust

employees and if organizational structures do not grant room for such activities in general.

Nevertheless, if organizational structures, culture, and corporate governance are overcoming

the identified barriers and implement respective enablers, they have a highly positive influence

on the usage of lean startup as a tool for digital BMI in established companies. Meaning they

make way for the frameworks’ next level – lean startup management. Thus, if established

companies have solid methodical competence, appropriate culture, organizational structure,

and dedicated corporate governance they facilitate lean startup management, such as handling

of MVPs, hypotheses, etc. This then results in successful usage of lean startup, which in the end

has a positive influence on digital BMI in established firms.

To sum up, our research examines that successful digital BMI in established companies with

lean startup depends on various influencing factors in six different areas. Moreover, it is


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important to consider interrelations between different areas of the framework, as strategic

actions might affect different fields of the framework. However, if all factors and areas of the

framework are considered, established companies are capable to successfully use lean startup

as a tool for digital BMI.

5.2 Digital Business Model Innovation with Lean Startup in Established Companies

5.2.1 External Influences

External influences, such as changing market conditions, changing customer behavior,

governance rules as well as role of competition, all originating from the development of digital

technologies, force especially established companies to rethink existing BMs as well as how

they engage with customers. Consequently, general change in markets and industries, and

digitalization as such, trigger a need to quickly adapt to changing environment by using agile

tools, such as lean startup. Following that digital shift to keep up with competition and to fulfill

novel customer and market needs can be a challenging task, especially for established

companies, as they must compete against digital native companies who are familiar in handling

digital technologies (Subramanian et al., 2011). Thus, development and introduction of digital

technologies results in an increasingly competitive landscape for established companies and is

threatening their established BMs (Ghezzi, 2020; Ghezzi & Cavallo, 2020). It also implicates

that a BM itself becomes an indicator for competitive advantage (Berends et al., 2016). This

leads established companies to change existing ways of value creation and capturing and

innovate their BM (Chesbrough, 2010) to compete with other disruptive players (Rejeb et al.,

2008). Thus, we argue that changing market conditions, triggered by novel digital technologies,

as well as role of competition, are enabling the usage of lean startup as an effective tool for

digital BMI in established companies. Nevertheless, these changing market conditions also

bring up challenges for established companies in terms of legal governance frameworks. These

outline that there is a need to break up existing, legal governance guidelines, as they impede

innovation processes with agile tools, such as lean startup. This aligns with Nirwan and

Dhewanto (2015), who found that existing regulatory and administrative regulations hinder

BMI processes with lean startup. However, legal governance frameworks on how to manage

innovation in established companies are difficult to directly influence for established

companies. Meaning, even if companies utilize all identified external influences of our

framework, they will not overcome the barrier of legal corporate governance regulation on their

own. Thus, legal governance can present an insurmountable barrier for using lean startup as


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tool for digital BMI in established companies. Therefore, it can be a limitation to our

framework. Nevertheless, we argue that with digitalization as such corporate governance

guidelines are forced to be adapted in novel digital market environment and innovations. Hence,

the pressure, created by developments in the market, will break up existing legal governance

frameworks automatically.

5.2.2 Methodical competence

Using lean startup for digital BMI implies a shift in the way established companies work

(organizational structures), think (culture), and act (corporate governance), to make them

capable of responding to changing market conditions faster and more agile (lean startup

management). From our empirical data, we understand implementation of solid methodical

competence throughout the entire organization as prerequisite. It becomes evident, that training

and educating all individuals (including management) within established companies is

essential, to build strong methodical competence of lean startup. Deep knowledge and

understanding how to use lean startup prevent established companies from frustration and

uncertainty, of which both are known to hinder innovation processes (Bocken & Snihur, 2020).

Thus, it also accounts for successfully using lean startup for digital BMI. As stated by our

empirical data, without expertise and knowledge of lean startup, the intention of digital BMI

tends to fail. Therefore, it is inevitable for established firms using lean startup for digital BMI

to change existing structures (organizational structures) in terms of education of novel, agile

methods such as lean startup. This also highlights how methodical competence is directly

influencing the framework’s next level – culture, corporate governance, and organizational

structures.

Based on the respondents’ opinions, not only theoretical knowledge, but most importantly

learnings generated by experimenting with and experiencing the method play a big role in

success of digital BMI with lean startup. This extends existing literature by Chesbrough (2010)

who argues that established companies learn through experimentation, and Berends et al. (2016)

stating that this builds an effective learning process to be innovative and to create novel BMs.

It further aligns with Bocken and Snihur (2020) who argue business experimentation not only

enables learning and innovation but also reduces uncertainty and helps to overcome

organizational inertia (organizational structures). To transfer this to lean startup as tool for

digital BMI, enabling knowledge flow about the method as well as creating room for individuals

to experiment with lean startup will help to reduce uncertainty and positively influence


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innovation processes. In the long run, this means that if methodical competence of all

individuals within established companies is strengthened, a solid foundation is formed for the

other areas (organizational structures, culture, and corporate governance) and thus enable

digital BMI with lean startup.

5.2.3 Organizational Structures, Culture, and Corporate Governance

Based on our empirical findings, organizational structures, culture, and corporate governance

play a vast influential role when using lean startup for digital BMI in established companies.

These three areas are highly interrelated and influence each other, as demonstrated in our

framework (figure 4). Therefore, it is important to understand them holistically and how they

affect each other.

As Berends et al. (2016) and Chesbrough (2010) argue, it is crucial to understand barriers of

BMI within the organizational context and to make necessary changes in leadership, culture,

and organizational structures. Hence, with our framework, we extent literature, exploring what

specific barriers of BMI occur within organizational areas in the context of digital BMI with

lean startup that requires established companies to align organizational structures, culture, and

corporate governance accordingly. It emerges that using lean startup in existing organizational

structures is challenging and will influence the entire organization. This is demonstrated in our

framework, as it is directly connected to culture and corporate governance and builds a ground

for lean startup management. Correspondingly, Xu and Koivumäki (2019) claim the biggest

challenge is formed by changing from existing to a more agile structure within a company.

Moreover, Berends et al. (2016) argue that existing organizational structures even lead to inertia

and therefore could hinder usage of lean startup in established companies. However, opposing

to stated literature, from our empirical data we found that existing organizational structure can

also be beneficial to change from a traditional to an agile structure. For instance, existing

resources can be an important facilitator for lean startup. Also, experimenting within a working

and save system can make the process more open and innovative. Hence, the save environment

of a big company can decrease pressure of being successful with lean startup and experiments

can go wrong in first place to learn from them. Meaning that organizational structures can

enable lean startup for digital BMI.

However, our empirical data align with Christensen (2003) stating that established companies

that are afraid of change and are stuck in their traditional structures (organizational structures)


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are less willing to experiment in terms of digital BMI. In this context, business experimentation

with agile methods, specifically lean startup, has become an important strategic element and is

used to generate and test novel BMs in a fast manner (Bocken & Snihur, 2020) which

significantly influences innovation processes of companies (Berends et al., 2016; Weissbrod &

Bocken, 2017). Regarding our study, business experimentation with lean startup is clearly

demonstrating, how not only methodical competence but also organizational structures,

culture, and corporate governance are interconnected and how they are forming barriers and

enablers to use lean startup for digital BMI in established companies. As business

experimentation with lean startup can help to overcome such organizational inertia (Bocken &

Snihur, 2020), it is important that existing organizational structures allow lean startup processes

and methods (Blank & Euchner, 2018). This means that for instance flat hierarchical structures

are needed as well as short ways of communication. Further, management (corporate

governance) need to have a positive attitude towards lean startup and needs to drive established

companies towards it. More specifically, the management is required to be committed to

achieve change with lean startup, needs to trust employees, should create a safe environment

for experimentation, and should be willing to delegate power. This then also requires a cultural

change of established companies to an open error culture (culture) for individuals to be able to

work with lean startup for digital BMI. In general, there needs to be more openness towards

novel solutions, a doer mentality within the company, strong intrinsic motivation as well as a

willingness to experiment. Hence, only if enabling aspects are implemented simultaneously in

all framework areas, a company will be able to successfully work with lean startup for digital

BMI. Yet, to simultaneously change corporate governance, culture, and organizational

structure of an established company is a highly challenging undertaking, as values, beliefs, and

certain ways of working have been existing for a long time and are difficult to change.

Moreover, companies that are in general facing changes due to the digital shift might be

overwhelmed by too many unknown methods and techniques. Therefore, the intent to change a

whole organization on different levels, is questionable and has to be considered as a limitation

to our framework. Nevertheless, if established companies aim to work with lean startup

successfully, the framework can still support them in planning consecutive strategic steps of

adapting enabling aspects in each area.

Ghezzi and Cavallo (2020) further argue that lean startup requires agility on every level in terms

of culture and organizational structures, as BMI, especially grounded in digital technologies,


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always forces a company to adapt the way they think and act. However, this also forms many

difficulties for established companies as it clashes with existing culture and organizational

structures. An enabling factor is to separate lean startup projects/units from core business to

successfully work with lean startup for digital BMI in established companies. This

argumentation is also supported by O’Reilly and Tushman (2004), who point out that separation

of innovation units and allowing these units to be different in their processes, structures,

leadership style, and culture is key to success. Hence, we argue that ambidextrous structures

(O'Reilly & Tushman, 2004) can be beneficial for companies using lean startup for digital BMI.

They do not have to necessarily transform the entire organization but can establish new

structures, culture, and management style beside their traditional business. In BMI literature,

ambidexterity and separation of business units are established concepts. Our study validates

these concepts and extents it to digital BMI with lean startup. Still, implementing ambidextrous

structures is a long-lasting process that forces change throughout the entire organization.

However, we argue it is a necessary first step for established companies that are facing a digital

shift and most suitable solution to work successfully with lean startup as a tool for digital BMI.

This way, both existing business (analog) and innovation unit (digital) can coexist and later be

combined, to move forward in the digital shift.

To conclude, only if established companies overcome outlined barriers and implement

identified enabling factors of this study in organizational structures, culture, and corporate

governance, using lean startup as a tool for digital BMI will be successful. These areas, as well

as methodical competence, form ground for the framework’s next level – lean startup

management.

5.2.4 Lean Startup Management

Our framework (figure 4) demonstrates that for lean startup management to be successful,

methodical competence needs to be implemented throughout the entire organization, and

necessary adaptions in organizational structure, culture, and corporate governance need to be

made prior. This is crucial for lean startup management, as without these changes, using lean

startup techniques and characteristics, such as handling of digital tools, MVPs, and hypotheses

as well as customer involvement, are likely to fail. In other words, enabling factors of the areas

below lean startup management (namely methodical competence, organizational structures,


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culture, and corporate governance) are pre-requisites for successful usage of lean startup tools

and techniques (lean startup management).

Established companies need to be prepared to work with lean startup as it forms a fast-paced

method for digital BMI. Moreover, it reduces distance between customers and established

companies, which otherwise can be a barrier for using lean startup in established companies.

The fast pace of lean startup and its build-measure-learn loops shall encourage established

companies to fail early in the process and not be afraid of failure as that is how they succeed

sooner (Brown & Katz, 2019; Ries, 2011). Therefore, it is inevitable for established companies

to have a fitting culture, an appropriate organizational structure, and dedicated management

(corporate governance) that support all techniques and tools of the lean startup process, such

as handling MVPs and hypotheses. Especially, regarding MVPs our empirical data show how

challenging it is for established companies as they hesitate to develop low quality or

“unfinished” products. Nirwan and Dhewanto (2015) describe it as fear of not satisfying

customer needs. But, as the lean startup process is highly customer-centric, derived empirical

data does not evaluate risk in customer satisfaction but rather in feedback quality as also

mentioned by Felin et al. (2019). According to Felin et al. (2019), it is hard to get valuable

customer feedback. Christensen et al. (2013) even argues that customers are not ideal to test

novel and radical BMs. Still, lean startup is about generating learnings from customers

(Mansoori, 2017; Xu & Koivumäki, 2019) and by involving them, established companies can

always learn something, in a positive (confirmation) or negative (rejection) way. Nevertheless,

it is crucial to identify and involve the right customers. Digital tools in this context are

facilitators, as they are enhancing customer engagement and involvement throughout the

process (Berman, 2012). From our empirical data, it emerges that lean startup is especially

useful in unknown markets and thus, emphasizes that customer involvement even helps

established companies to explore the novel digital environment, new customer needs, and

requirements. Therefore, we argue that effective lean startup management helps established

companies to master the digital shift with lean startup as a tool for digital BMI.

To conclude, using lean startup in established companies is difficult. However, if lean startup

management is done successfully, the user-centric, fast, and resource-efficient approach, as well

as iterations with customers, will lead to development and continuous improvement of digital

BMs. Nevertheless, it is necessary to not see lean startup as a generally successful tool for

digital BMI, as it has to be evaluated in the respective context. Therefore, it is important to


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holistically understand the framework (figure 4) for lean startup management to successfully

use lean startup for digital BMI in established companies.

6. Conclusion & Discussion

___________________________________________________________________________

This section answers the research question and presents the main conclusions of our study. It

highlights our theoretical contributions, and practical implications. Further it outlines

limitations to the study and suggests future research. ______________________________________________________________________________________________________________________________________________________

6.1 Summary

Established companies, that have been successful in their known business environment for a

long while, are now being challenged by changing market conditions triggered by digitalization

and development of digital technologies. Hence, there is a need for these companies to reinvent

their BMs by adding digital technologies and digitally shift their existing business. With our

work, we introduce lean startup as an effective tool to digitally reinvent BMs, as it is a fast-

paced and resource-friendly method. We identified enablers and barriers of lean startup as a

tool for digital BMI in established companies. Further, we proposed a framework showing how

established companies can be successful in digital BMI using lean startup. Hence, we

emphasize that successfully using lean startup for digital BMI is based on an effective (1) lean

startup management, appropriate (2) organizational structures, fitting (3) culture, and

dedicated (4) corporate governance, which all require solid (5) methodical competence on all

organizational levels. Furthermore, (6) external influences such as market conditions, role of

competition, or governance rules indirectly affect the usage of lean startup as a tool for digital

BMI. We argue that all areas withhold different aspects that can either be enablers or barriers

to use lean startup as a tool for digital BMI in established companies. Thus, we introduced a

framework (figure 4) showing how these elements can both facilitate or hinder using lean

startup for digital BMI as well as displaying connections between different influential factors.

Basic principle for established companies is to identify hindering aspects, adapt identified

enabling factors and hence overcome existing barriers.

Our study reveals that using lean startup for digital BMI implies a shift in the way established

companies work, think, and act, to respond to fast-changing market conditions and to stay

competitive in the digital market environment. Using lean startup is therefore especially

difficult for established companies since internal processes, corporate and personal culture as

well as management and leadership style have been existing in organizations for a long time.


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Moreover, these factors combined can lead to corporate inertia which lean startup helps to

overcome. With our study, we contribute showing what strategic actions need to be taken to

overcome inertia and to successfully work with lean startup as a tool for digital BMI. First,

companies need to implement methodical competence on all organizational levels. Then,

necessary adaptions on different levels of organizations, namely culture, organizational

structures, and corporate governance, need to be made. Our study highlights that these changes

in above-named areas are to be understood holistically, as these areas are all interrelated. Thus,

making strategic changes in one area can directly influence the others. Yet, simultaneously

changing established organizational structures, culture, and corporate governance is a long-

lasting and challenging task. Nevertheless, our framework gives guidance on how to

consecutively plan necessary steps for changes to positively influence a digital shift with lean

startup. To conclude, if all derived factors from our framework are considered, organizations

build a beneficial foundation for successfully using lean startup as a tool for digital BMI. This

will help established companies to digitally shift and regain their competitive position in the

novel digital market environment.

6.2 Theoretical Implications

As outlined in the problem discussion, there is a research gap about using lean startup for BMI

in established companies. With our study, we attempted to address this gap, enriching theory

of lean startup and BMI, as well as digital BMI, in established companies. In this part, we

illustrate our main theoretical implication and contributions to existing literature within these

three fields.

With our derived framework, we enrich current literature on BMI in established companies, as

the empirical data help to identify barriers and enablers of BMI using lean startup within

established companies. Scholars in this field argue that it is essential to identify and understand

barriers of BMI within organizations and to make necessary changes in leadership, culture, and

organizational structures accordingly (Berends et al., 2016; Chesbrough, 2010). With our

empirical data, we provide deep insights about specific aspects within established companies

that form inhibiting and enabling factors for BMI. Moreover, with our framework, we explore

areas within organizations that form barriers, and outline which strategic actions need to be

considered in order to overcome them. In addition, we demonstrated how all factors are

interrelated and how changes in one field might influence others. In this sense, we substantially

enriched existing literature of BMI in established companies.


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Further, we enrich existing theory on digital BMI. With our work, we provide novel insights

about how established companies can use lean startup to digitally transform their existing BMs.

However, as stated by literature on digital BMI, especially companies that have been establised

leaders in the non-digital world, face extrem difficulties when digitally transforming (Berends

et al., 2016; Christensen, 2003). Yet, we enrich existing literature, showing that lean startup is

already successfully used by many established companies in the context of a digital shift. Due

to our derived framework, we provide deeper understanding on how companies can quickly

undergo a digital shift by using lean startup.

Moreover, our research followed the request by Ghezzi (2020), Ghezzi and Cavallo (2020),

Leatherbee and Katila (2020), as well as Edision et al. (2018) to provide more detailed,

empirical evidence of using lean startup in established companies. As lean startup has mainly

been investigated as a tool for BMI in digital entrepreneurship (Ghezzi, 2020; Ghezzi &

Cavallo, 2020) there is still a need to examine how lean startup can be successfully used as a

tool for BMI in established companies. We fill this research gap by extending understanding of

how established companies can adapt agile structures to drive digital BMI to stay competitive

in the digital context. Current literature in this field states that established companies still

struggle with lean startup (Blank & Euchner, 2018; Bocken & Snihur, 2020; Xu & Koivumäki,

2019). With our empirical findings, we provide a deeper understanding for established

companies to master usage of lean startup for digital BMI, and hence enrich the research field

with novel insights.

6.3 Managerial Implications

In this section, we illustrate four practical implications for managers in established companies

involved in the fields of BMI, business experimentation as well as usage of agile tools, such as

lean startup for BMI. These implications are especially relevant for managers of established

companies that have enjoyed a strong and successful market position but are now facing fast-

changing market conditions triggered by the development of digital technologies and

digitalization.

To understand the topic holistically, we recommend managers to realize that lean startup needs

to be understood as an end-to-end solution for digital BMI and growth(Blank & Euchner, 2018),

which requires necessary adaptions on different levels of the organizations, namely culture,

organizational structures, and management. Therefore, successful digital BMI must overcome


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identified barriers, such as high hierarchical structures, long ways of communication, traditional

mindsets, missing persistence, fear of change, and traditional leadership styles to work with

lean startup for a fast-paced innovation in digital BMI. At the same time, managers should try

to adapt and change considering enabling factors, such as using existing company resources,

implementing ambidextrous structures, flat hierarchies, high willingness to change, experiment

and learn, an open error culture, fostering a doer mentality, strong commitment from top level,

providing a safe environment and freedom for employees as well as open and transparent

communication. If managers consider all derived factors from our framework, organizations

build a beneficial foundation to successfully working with lean startup as a tool for digital BMI.

By using our framework managers have a comprised overview as a base for strategic activities

and decisions. Thus, managers can assess, decide on, and prioritize necessary actions to take

and areas to consider to diminishing barriers when using lean startup for digital BMI. Moreover,

it is crucial for managers to consider connections between different areas of the framework, as

strategic actions taken by management might be connected to different fields of the framework.

By focusing on activities deriving from our framework, managers can build a novel foundation

in established companies to drive digital BMI in a faster, more resource-efficient, and more

customer-centric manner, which in the long run helps to make their positioning viable as well

as to thrive in the novel, digital environment.

From our empirical data, we learned that especially management and leadership style in

established companies play a crucial role for using lean startup as a tool for digital BMI.

Therefore, it is beneficial that the management is fully dedicated to lean startup and its tools

and techniques. Hence, management teams need to lead by example. Imperative in this context

is the management’s openness to lean startup and to delegate power to employees. Moreover,

it is necessary to change from a rather traditional leadership style, where control is in focus, to

a more agile leadership style, where managers inspire and trust their employees.

Also, we highlight the importance of establishing methodical competence throughout the entire

organization. It builds a solid foundation for successful usage of lean startup in established

companies. Hence, managers need to provide opportunities for their employees to familiarize

themselves with the tool. This needs to happen on a theoretical level, but most importantly by

providing room and time for experimentation to gain practical experience with lean startup.


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However, lack of knowledge needs to be overcome by employees and management alike. From

our empirical data, it became apparent that involvement of external lean startup coaches and

experts are beneficial to close the knowledge gap. This way, the entire organization builds novel

methodical competence in handling new, agile solutions. It also reduces uncertainties,

facilitates handling of agile tools, such as lean startup, and also fosters innovation of digital

BMs. As a result, established companies can strengthen their competitive position in the still

unfamiliar digital environment and build a profitable future.

6.4 Limitations and Future Research

Although we answered our research question proven with empirical findings concerning the

usage of lean startup for digital BMI in the context of established firms, this study entails

limitations and thus calls for further research.

One limitation to our research relates to transferability (Guba, 1981) of our empirical findings

in another context. As we only examine individuals working with lean startup for digital BMI

in the German market, we cannot draw conclusions to other markets and environments.

Additionally, it remains unclear if our framework applies to a wider range of international

organizations within fast-changing markets. Hence, we suggest further research. Using a similar

research design in a different context and compare empirical findings with our derived

framework, could be beneficial to indicate similarities and differences.

To gain deeper insights into lean startup in established companies for digital BMI, we furtherly

recommend conducting case studies of diverse established companies using lean startup. Thus,

more primary data is generated which will help to sharpen the insider perspective of individuals

working with lean startup. We suggest conducting further research on the role of management,

because, as indicated from our empirical insights, they play a major role when using lean startup

in established firms.

It also became clear that lean startup is oftentimes used in combination with other agile tools,

such as design thinking. Thus, a limitation of this study is that lean startup is seen as a stand-

alone tool for digital BMI. Hence, we call for further research of using other agile tools and

combination of such tools for digital BMI in established companies. As we derived a framework

that applies to the usage of lean startup, it appears that same could account for general usage of


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agile methods. Hence, it is necessary to explore our framework in comparison to other agile

tools to examine if it is also applicable in those areas.

In general, our framework shows some limitations. As outlined, it is questionable if established

companies that face a lot of uncertainty triggered by a fast-changing market environment and

development of digital technologies are able to internally change organizational structure,

culture, and corporate governance simultaneously. Moreover, because our framework explores

interrelations of many different areas, it might not only inhibit change in one organizational

area (e.g. organizational structure) but become an insurmountable obstacle in all areas. Thus,

the intent to use lean startup for digital BMI to digitally shift a company might not be successful.

Hence, further research is required on how established companies can simultaneously change

these areas without being overwhelmed by the whole process and pressure of the digital shift.

Furthermore, we recommend conducting a longitudinal study of established companies in the

digital transformation process, to better understand the development of digital BMI with lean

startup in established companies over time. As we see the usage of lean startup as a tool for

digital BMI as part of a digital shift of established companies, we argue that it is a long-lasting

process. This also implies that our empirical findings can change digital transformation

advances over time. Hence, we recommend to furtherly explore this phenomenon and to get a

deeper understanding how lean startup can be deployed sustainably as a tool for digital BMI in

established companies.


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Appendices

Appendix 1: Interview Guideline

Themes Leading Questions Follow Up Questions Optional Questions

Part 1:

General

Questions

1. Brief introduction: Who are

you and what do you do for a

living, what is your main

focus?

2. (To what extent do you

work with lean startup and)

what do you understand by

the term lean startup?

3. How is lean startup applied

in established companies or

how do you apply it?

How does lean startup differ from

other methods of digital business

model innovation?

What does digital business

model innovation mean to

you?

With what type / size /

industry of company do you

apply lean startup?

Part 2:

Digital

business

model

innovation

4. For which type (s) of digital

business model innovation is

the lean startup method

particularly suitable in your

opinion? / How is lean

startup applied in the area of

digital business model

innovation?

How do you evaluate lean startup

as a tool for

BMI for established companies?

What role does lean startup

play in digital business model

innovation in established

companies?

How is the use of lean startup

related to digital business

model Innovation?

To what extent can and must

innovative lean startup units

be separated from the core

business?

Digital

Technologies

5. What role do digital

technologies play when it

comes to lean startup as a

tool for digital business

model innovation?

How do digital transformation

and application of Lean

Startup relate?

Success

Factors 6. What pre-

conditions should bring a

company in your opinion, to

work successfully with Lean

Startup?

How does the application of lean

startup in established companies

differ from the application in

growth companies / startups?

Positive

Aspects

7. How do you feel about the

cooperation with the

established companies and

lean startup?

What are positive experiences

that you have made in connection

with lean startup in existing

companies?

In your opinion, what are the

success factors in the

implementation of lean startup in

established companies?

Which factors favor the use of

lean startups in established

companies?

What works well in the use of

lean startup in established

companies?

Barriers 8. What are common problems

/ complications or risks that

Where do you see potential for

improvement in the application of

Where do obstacles / barriers

regularly occur in the

application /


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arise when using lean startup

with existing companies?

lean startups in established

companies?

Which factors hinder / complicate

the application of lean startup in

established companies?

implementation of lean

startup?

Handling of

Lean Startup

tools

9. How do employees /

management

perceive the customer-

centered approach or

the open-ended, experimental

approach of lean startup?

What role does top management

play in the successful

implementation of lean startup?

What are your experiences with

handling hypotheses? How are

“good” hypotheses generated?

How is customer feedback

incorporated?

How do employees perceive the

iterative build-measure-

learn loops?

What is the role of the

employees?

What are the benefits of

engaging customers?

What are the downsides to

engaging customers?

When and how do you decide

that the process is complete?

How are employees in

existing companies

introduced to the Lean

Startup method?

How is or can method

competence for the

application of lean startup in

companies be established?

Corporate

Culture

10. What influence do the

corporate culture aspects of

the established companies

have on the implementation

of lean startup?

To what extent does the use of

lean startup bring about a cultural

change in existing companies?

What role do the existing

organizational structures and

processes play in existing

companies for the successful

implementation of lean startup

methods?

To what extent does the

willingness of companies to

change play a role? / To what

extent is this important for

digital business model

innovation with lean startup?

Final

Question

11. What are the three most

important learnings about

lean startup application in

established companies for the

innovation of digital business

models that you would like to

advise us?


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Appendix 2: Consent Form


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Appendix 3: Overview of all Codes, Sub-Categories, and Categories

Code Sub-Category Category

1 Combining methods

Agility

Lean Startup

Management

2 Discard ideas

3 Explorative approach

4 Generating learnings

5 Intuitive process

6 Iterative process

7 Risk management

8 Open-ended process

9 Resource-efficiency

10 Facilitator

Digital Tools 11 Importance of digital tools

12 Scaling potential

13 Customer centricity

Customer Involvement 14 Customer distance

15 Customer feedback

16 Creating hypotheses

Handling of Hypotheses 17 Testing hypotheses

18 Validating hypotheses

19 Building MVP

Handling of MVP

20 Identifying core element

21 Forms of MVP

22 Quality of MVP

23 Role of MVP

24 Separation of lean startup projects

Organizational Structures

25 Changing organizational structures

26 Changing procedure

27 Existing organizational structures

28 Flat hierarchies

29 Agile culture

Corporate Culture

Culture

30 Competitiveness

31 Cultural change

32 Error culture

33 Openness

34 Role of corporate culture

35 Seriousness

36 "Doer" mentality / just do it

Mindset

37 Attitude

38 Changing mindset

39 Persistence

40 Traditional mindset

41 Willingness to change

42 Willingness to experiment

43 Willingness to learn

44 Employees driving change Motivation


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45 Intrinsic motivation

46 Volunteers

47 Courage

Personal culture 48 Fear

49 Small degree of arrogance

50 Providing boundary conditions

Leadership

Corporate Governance

51 Creating save environment

52 Equality

53 Freedom and independence

54 Guidance

55 Motivation of employees

56 Open and transparent communication

57 Trust

58 Attitude of management

Management

59 Influence of management

60 Lack of foresight

61 Loose of power

62 Success-oriented

63 Dedication/commitment

64 Knowledge transfer

Knowledge

Methodical Competence

65 Lack of knowledge

66 Practical experience/prior knowledge

67 Theoretical knowledge

68 Overcoming uncertainty

69 Diverse capacities and expertise

Teaming 70 Interdisciplinarity

71 Mixed teams

72 Education of employees

Training/education 73 Education of management

74 External coaches

75 Multiplicators

76 Changing customer behavior

External Influences 77 Changing market conditions

78 Role of competition

79 Role of governance rules

lean startup as a tool for digital business model

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