Industry 4.0: Value Generation and Adoption of Digitalization and Industrial IoT in Production

SAMINT-MILI 2039

Master’s Thesis 30 credits

June 2020

Industry 4.0: Value Generation and

Adoption of Digitalization and

Industrial IoT in Production

The Case of Swedish Production Focused

Companies in Mälardalen

Zoran Taloyan

Abstract

Industry 4.0: Value Generation and Adoption of

Digitalization and Industrial IoT in Production

In a historical sense, technology has always been used to find practical solutions to certain problems. From the development of heat- and steam engine, to the new revolution of Industry 4.0. Technological advancements are in today’s society becoming more autonomous and automated, with the generation of- and utilization of Big Data. This is mainly the reason for the development of technologies such as “Internet of Things” (IoT) and its adoption and value generation for the manufacturing industry. The technology of IoT, when implemented in an industrial context, are often times refers to as Industrial Internet of Things (IIoT). As novelty of technology is often times, well researched in a technological sense, the purpose of this thesis has been to extend the understanding of value generation and creation within the context of Industry 4.0 for production focused companies, as well as to find the drivers of adopting IoT into manufacturing. As the thesis is a qualitative study based on prior scientific journals regarding this topic and with data collection from five in-depth interviews, the research framework that has been pursued, has been according to Grounded Theory. The process from raw data, through the creation of 1st order concepts and 2nd order themes,

the resulting findings, has shown that the manufacturing industry finds its value generation and, drivers of adoption within four dimensions that this thesis has concluded: Competitiveness, Optimization, Veracity and Control. From the identified drivers of this thesis, main value are generated through improved data-driven decision-making and meeting future customer demand. Other value generators, are found within optimization of mainly resource and machine optimization within the actual production. Ultimately, where value are being generated with IoT adoption are many, but decreasing risks associated with Supply Chain and transportation are together with above mentioned value generators, where manufacturing firms are find their increased value generation with Industry 4.0 adoption and what drives the manufacturing companies to adopt technologies such as Industrial Internet of Things.

Keywords: Big Data, Internet of Things, Value Generation, Drivers of Adoption,

Manufacturing Industry

Supervisor: Tomas Rydh Subject reader: Simon Okwir Examiner: David Sköld SAMINT-MILI 2039

Printed by: Uppsala Universitet Faculty of Science and Technology

Popular Science Summary

Technology has always been used to find practical solution to certain problems that humans have been encountering for decades and centuries, from the first development of the steam- and heat engine to today, where the emergence of Industry 4.0 has been developed. The transition from manual labor to mechanical power has played an important role in the development of human society. Society saw these changes and revolutionary technical advancements as major, to the extent that we named them specific terms for their respective impact. From the 18th century, Industry 1.0 was coined, and when we fast forward, today we are again on the brink of a new Industrial Revolution, namely: The fourth Industrial

Revolution. While technology evolved and is continuously being developed, the complexity of technology increases with it. Today, human societies are finding themselves in the

presence of technologies such as Artificial Intelligence, Internet of Things, Cloud Computing to name a few that are included in the fourth industrial revolution. These technologies are and can be utilized within manufacturing and production firms, as the technology of Internet of Things, are compiled by a multilayered structure, which means that the technology of IoT are built on-top of other already existing and implemented technologies. With underlying

technologies such as Wireless Sensor Networks (WSN) which are sensors that constantly collect data in real-time, machine-to-machine communication (M2M), which interact and “talk” to each other in a digital way and Cyber-Physical Systems (CPS), which merge the physical world with the digital world. Big Data, which is information that shows patterns within the data, which in return can be translated into new business opportunities for

organizations’. Big Data, that these sensors generate, IoT brings value in having the access to real-time data and improving decision-making for organizations. As value generation is not entirely clear, when manufacturing firms decide to adopt the technology or not, this study intended to investigate mainly how value is being generated through technologies included in Industry 4.0. To find the value generation Industry 4.0 contributes within manufacturing firms, drivers of adoption were pursued to investigate. To understand the impact that new technological advancements can have, two different data sources were utilized. The first data source was to find out what scientific literature are concluding in domains of digital

Acknowledgements

This master thesis has been written in collaboration with multiple actors that have either contributed or helped the researcher in his quest to further research within the topic, of which the thesis is pursuing. The research has been written independently by Zoran Taloyan, who want to show his deepest gratitude to the following actors/contributors that still were involved in the process of writing this thesis, even when major hardships struck our society and our everyday lives changed, such as during the Covid-19 crisis.

First, the researcher wants to show his greatest gratitude to Simon Okwir, whom has been the subject reader and a major helper in mainly structuring, forming the thesis and showing direction on how to analyze and create the data structure and analysis. Simon Okwir has always been available and helped when obstacles have risen in the process of writing the thesis, in a pedagogical and clear way. A major thank you to Simon Okwir.

Second thank you is towards Tomas Rydh, who was the supervisor from the company Avalon Innovation. Tomas Rydh mainly helped in the data collection-stage and finding interviewees of interest, together with working as a comforting function for whatever questions that has risen during the process of this thesis. Tomas Rydh are highly appreciated by the researcher, and a big thank you to both Tomas Rydh and Avalon Innovation for the contribution to this thesis.

Third, deepest thank you to all participating interviewees that still chose to let the researcher to ask his questions and to contribute to this thesis with their time and input, even when Covid-19 affected all our lives. The researcher wants to show his greatest gratitude and say a big thank you, and as the participants chose to stay anonymous, each and every contributor that participated, you know who you are, a huge thank you.

Fourth, the researcher want to thank David Sköld for both his feedback from the last

presentation and thesis-defense session. The feedback was utilized to enhance the structuring and the content of the thesis, so therefore; thank you David.

Last, the researcher wants to thank Uppsala University and the Department of Civil and Industrial Engineering, for a great time during the course of the study and all the lecturers, teachers and professors within the department, that has been involved in my study and my quest of deepening my knowledge in Industrial Management and Innovation.

Uppsala, 11th June 2020

Table of Content

Popular Science Summary ... 3

Acknowledgements ... 4 List of Figures ... 7 List of Tables ... 7 Abbreviations ... 8 1. Introduction ... 9 1.1 Background ... 9 1.2 Problematization ... 11

1.3 Purpose and Research Questions ... 12

1.4 Delimitations ... 13 1.5 Structure of Thesis ... 13 2. Literature Review ... 14 2.1 Innovation Theory ... 14 2.1.1 Drivers of Innovation ...14 2.1.2 Adoption Theory ...15 2.2 Digital Transformation ... 16 2.2.1 Impact on Customers...18

2.2.2 Impact on Organizational Structures, Processes and Routines ...19

2.2.3 Impact on Business Models ...20

2.3 Industry 4.0 and Servitization ... 21

2.4 Value Creation ... 25

2.4.1 Firm Value Creation ...25

2.4.2 Value Co-creation...27

2.4.3 Customer Value Creation ...28

2.5 Summary and Process of Literature Review ... 28

3. Methodological approach... 31

3.1 Research Context and Design ... 31

3.2 Data Collection ... 33

3.2.1 Sampling ...33

3.2.2 Interviewing ...33

3.2.3 Documents...34

3.3 Data Analysis ... 36

3.3.1 Development of Grounded Theory ...36

3.3.2 Process of Thematic Analysis ...38

4. Empirical Results ... 42

4.1 Thematic Analysis – 1st order Concepts and 2nd Order Themes ... 42

4.2 Data Structure ... 46

5. Analysis ... 48

5.1 Competitiveness ... 48

5.1.1 Company Reputation and Branding ...48

5.1.2 Managerial Decision-Making ...49

5.1.3 Customer Specific Requirements and Demands...50

5.2 Optimization ... 51

5.2.1 Productivity...51

5.2.2 Resource and Machine Optimization ...51

5.2.3 Time Management ...52

5.3 Veracity ... 53

5.3.1 Veracity Data ...53

5.3.2 Monitor of Working Environment ...54

5.3.3 Pressure and Stress relieving function ...55

5.4 Control ... 56

5.4.1 External Stakeholders ...56

5.4.2 Transport and Supply Chain ...57

5.4.3 Predictability ...58

6. Discussion ... 60

6.1 Drivers and Value Generation of IoT Adoption ... 60

6.2 Ethical and Societal Implications ... 67

7. Conclusion ... 68

8. Self-Reflection and Further Research ... 70

9. Limitations ... 71

10. References ... 72

Appendices... 77

Appendix A: Interview Guide ... 77

Appendix B: Data Structure ... 80

List of Figures

Figure 1. Historical representation of technical development and increased complexity in industry adaptation and transformation (Ślusarczyk, 2018, p.236). ... 10 Figure 2. Digital Transformation and it’s relationship and dependency on Digitalization and Digital Innovation (Osmundsen et al., 2018, p. 2). ... 18 Figure 3. Suggested roadmap for implementing Digital Transformation into business models

(Schallmo et al., 2017, p. 8). ... 20 Figure 4. Concept and application of IIoT between physical and digital systems (PLM refers to Product Lifecycle Management) (Jeschke et al., 2017, p. 6). ... 23 Figure 5. Multilayered approach to implementation and utilization of Industrial IoT (Pticek et al., 2016, p. 6). ... 24 Figure 6. Perceived benefits and value in using IIoT across multiple industries (Kiel et al., 2017, p. 19) ... 27 Figure 7. The iterative and flexible approach to the literature review, that the researcher of this study worked according to. ... 30 Figure 8. The main outline of the stages qualitative studies are pursuing (Bryman & Bell, 2011, p.390). ... 32 Figure 9. Data Structure. ... 47 Figure 10. Identified drivers of IoT adoption for manufacturing and Production focused companies and its respective value generation. ... 66

List of Tables

Table 1. Identified characteristics by Roger’s: Diffusion of Innovation, concerning decision-making in adopting a new innovation or not (Murray, 2009; Rogers, 2003). ... 16 Table 2. Background on the participants and general information on setting and timeframe regarding the interviews. ... 34 Table 3. Five scientific documents with conclusive texts for later triangulation with the results that this thesis has concluded. ... 36 Table 4. The features and stages that enhanced the development of grounded theory in this project... 38 Table 5. A scaled-down presentation of 1st order concepts and 2nd order themes from the conversations

Abbreviations

IT

Information Technology

IoT (IIoT) Internet of Things (Industrial Internet of Things)

CPS

Cyber Physical Systems

CPPS

Cyber Physical Production System

AI

Artificial Intelligence

M2M

Machine-to-Machine

DTF

Digital Transformation Framework

WSN

Wireless Sensor Network

DT

Digital Transformation

DE

Digital Eco-system

4IR

Fourth Industrial Revolution

HAC

Human-Agent Collective

IS

Information System

B2B

Business-to-Business

B2C

Business-to-Consumer

TBL

Triple Bottom Line

1. Introduction

In the introduction the background of the study is presented, together with the problematization, purpose and research question, together with delimitations. The introduction also provides an introduction to the technology that are being researched; Internet of Things and the concept of Industry 4.0.

1.1 Background

Historically, through scientific discoveries, technology has been advanced to find practical solutions to certain problems in several domains. One of the most significant and early

technological discoveries that was developed, was the transition and utilization of mechanical power instead of using human labor for certain industrial tasks. This is where the steam- and heat engine makes its entry into human society and history (McNeil, 2002). As the time proceeded, technology and science continuously developed, that is where we ultimately named those changing eras of technology and science from the initial industry 1.0 to today’s technological concepts and developments: The fourth industrial revolution, which is also recognized, by the name Industry 4.0. This concept is defined as an interdisciplinary

concepts, with the integration of communication and information technology in the context of industrial manufacturing and environment (Horváth & Szabó, 2019). Figure 1 shows the progression of industry 1 to industry 4.0 since the 18th century until today (Ślusarczyk, 2018; Weyer et al., 2015).

Figure 1. Historical representation of technical development and increased complexity in industry adaptation and transformation (Ślusarczyk, 2018, p.236).

The development of emerging technology such as Industrial IoT, has provided industries and production focused companies in Mälardalen, Sweden, the possibility with a new technology to improve their operations, in terms of what the possible value generation of implementing and adopting IIoT could be. There is a lack of research in how Industrial IoT can increase and create value, and if the targeted companies are ready for DT of their operations and if these companies are ready for adopting a new and emerging technology like IIoT. So, the study is therefore pursuing and investigating these specific topics of interest, for answering the research question, which are posed in section 1.3 Purpose & Research Questions.

Individual technologies included in 4th Industrial Revolution, have a wide range of areas of multiple possible implementations, hence, this study is interested in and investigating particularly IoT and its adoption and value into production (implemented and called

Industrial IoT (IIoT) in the context of production). As the technology itself is built on-top off already existing digital infrastructures, IIoT has a multilayered structure which can be

the aim is to extent our knowledge on the impact that these technologies can have in the context of Swedish manufacturing companies.

1.2 Problematization

The vast literature on diffusion of Innovation (Rogers, 2003), has documented how

innovation ebb and flow. Through adoption theory, the literature on diffusion of innovations gives explanations how different types of adopters behave with new innovations (Rogers, 2003). However, while such explanations have been put forward and accepted, they inform us very little about value generation. As such, with the current digital transformation, several technologies are diffused but less understood on how value is generated as already believed in practice. For example, technologies in Industry 4.0, are generally believed to transform and create value in different areas within and outside the firms, that are pursuing these

innovations. As Cantwall (2001) describes in his study: “Thus, innovation depends upon the

generation of feasible new capabilities, the operation of which adds new value to the existing circular stream of income, and thereby creates new profits and higher income” (Cantwell,

2001, p. 2).

While this is a generally accepted fact, not so much has been done on the progress of value capture, value creation and value proposition in Industry 4.0. What is being seen in practice are competing models and rather a performance in ignorance, trial and error led perspectives.

There are three important dimensions that this thesis attempts to challenge. First is decision-making, as opposed to prior use of past data in organizations for their decision-making. However, in today’s context of Industry 4.0 brings decision making to a new age, this process can be automized and decentralized (Haddud et al., 2017; Hartmann & Halecker, 2015). The assumption is that Industry 4.0 technologies contribute to faster and more significant

decision-making. In order to increase the productivity and efficiency in manufacturing companies (Horváth & Szabó, 2019; Kiel et al., 2017), it is apparent that the process of decision-making enabling better business performance is revisited. For example, how decision-making impact and value generation for customers. This leads to changes in business models “fitting” as a result of Industry 4.0 technologies (Horváth & Szabó, 2019).

Second, is the impact on organizational structures, processes and routines, Industry 4.0 has

allowed new forms of value creation. Traditional backward-looking to the age bureaucratic organizations, the adopted structures and routines were naturally distributed alongside hierarchies in which information and decision-making was a strict top-down flow. Routines and capabilities emerged as non-immutable and organically breaded with each functional area. However, within the context of Industry 4.0, technologies have broken strict hierarchies where contracts become smart contracts and decision-making is now digital decision-making. Additionally, technologies have made it possible to allow new entrants to the systems

Third is Supply-Chain Management and how value is being generated through closer

collaboration between multiple actors in the value- and supply chain of manufacturing

companies (Birkel & Hartmann, 2019). What is being questioned in its assumption is if closer collaboration in supply chains and value chains are contributing to improved performance and value generation with the implementation of Industrial IoT. Many scholars argue for Industry 4.0 and its beneficial technology which impacts the manufacturing industry in areas of mainly operations, services and products, practically in every aspect of the supply chain (Horváth & Szabó, 2019). This change is believed to present itself from the initial product development, to the actual manufacturing process, and finally to the product distribution, which are believed to be affected and will change (Koh et al., 2019). This goal of stages included in product distribution is intended to deliver products and services to firms’ customers, therefore, the assumption of Industry 4.0 technologies impacting and benefitting organizations customers are heavily researched, but where value is being generated is not as obvious as it seems. While organizations are trying to find the right balance between pricing, quality and product delivery towards their customers, many scientific journals emphasize on shorter delivery time, and having the possibility to respond to customer-specific requirements (Bauer et al., 2015), which are areas where value is being generated for customers.

1.3 Purpose and Research Questions

Based on the above assumptions in the problematization, the overarching purpose of this thesis is to investigate the process of value generation within the context of Industry 4.0. In order to reach this purpose, the study’s focus is on production focused companies’ adoption of IoT technology. By understanding the current strategies production firms use in decision-making and how they organize their processes and routines through IoT, the thesis attempts to track how value generation can be described within such context. As such, the thesis sets out to answer the following Main Research Question (MRQ):

MRQ: How is value generated through Industry 4.0 in manufacturing firms?

To answer the MRQ, the thesis seeks to investigate first, the drivers of adopting Industry 4.0 and hence expand to understand how value is generated. The process allows a new Sub Research Question (SRQ) as presented below.

SRQ: What are the drivers for adopting Industry 4.0 technology in manufacturing firms?

1.4 Delimitations

There are delimitations realized in this thesis, first is the way how the study was conducted, the involvement of one external partner and “industry-player”: Avalon Innovation. Which can result in reluctance from the participants to share insights or reluctance from sharing to much relevant information. Second, there is also the geographical limitations to this study, where the focus of sourcing the empirical data collection, came from the Mälardalen-area, in

Sweden. This will prone the study to be not generalizable to the production industry in a wide applicable purpose to industries in general. This issue of not being able to generalize the result and-/or findings is also one of the biggest limitations of conducting a qualitative study (Queirós et al., 2017) together with having a limited number of cases and organizations to study. Third, another delimitation in this study and subject of interest is Industrial IoT, is concerning the coverage of the technology itself. The concept of Industry 4.0 and the technologies that are included, are widely applicable to every part and aspect of an organization and is not entirely understood and consistent in terms of scientific rigor, especially regarding Internet of Things (IoT).

1.5 Structure of Thesis

The rest of the thesis project is organized as followed. In Chapter 1, there is first an

introduction to the background of the research topic that this project is investigating, together with problematization, purpose & research questions, and delimitations with the study. The second chapter is the literature review where in-depth understanding is processed within areas of relevance and interconnectivity of Industrial IoT. The areas of interest are: Drivers of Innovation Adoption, DT, Servitization & Industry 4.0 and Value Creation, since the thesis pursues to find the value generation IoT contributes with in a manufacturing context. The

third section is the methodology-chapter, with extensive Research Design & Context,

2. Literature Review

Section below explores literature studies in the field this study is investigating and finding relevant, which is within Industrial IoT and Industry 4.0. As the research questions is posed, to find the value generation of an Industry 4.0 technology together with drivers of adoption for production focused companies. This section reviews literature in four (4) main domains; within Drivers of Innovation, Digital transformation, Industry 4.0 & Servitization and Value Creation Theory.

2.1 Innovation Theory

Within Innovation Theory, and its domains of intersection, there are two areas of

investigation that are needed for gaining a fundamental understanding of innovation and its adoption within the context of manufacturing industry and its adoption of Industry 4.0

technologies. Those are Drivers of Innovation, together with Adoption theory, which is being reviewed in this sub-chapter.

2.1.1 Drivers of Innovation

As the Sub Research Question (SRQ) is investigating the drivers of adopting IoT into the manufacturing context, the natural area of investigation is therefore with the drivers of innovation and what driver of adoption is, for new technologies and innovations.

Joseph Schumpeter was a well-known and one of the most renounced economists that contributed to Economic Theory in subjects of entrepreneurship and innovation, where innovations where a cornerstone for economic development and being able to break the “static mode” on an organization’s economic flow. These new combinations and innovations were the vehicle according to Schumpeter, for being revolutionary and discontinuous within an market. The word “Innovation” has been referred to something unusual during the 1880s and Schumpeter’s contributions were of wide influence and development of Innovation Theory (Śledzik, 2013). But for the definition of “Innovation”, we enter the realm of another big contributor to Innovation theory, which was Everett M. Rogers, with his Diffusion of Innovation Theory (Rogers, 2003), where he described innovation being:

“Innovation is an idea, practice or object that are perceived as new by an individual or other

units of adoption” (Rogers, 2003, p. 12).

Schumpeter had the notion and idea that without innovating continuously, organizations won’t be able to gain profits, as innovations are the essential driver in an competitive

innovation, are being driven by seeking out different opportunities of value-creating and generating activities for increasing organizations circular flow of income, according to Schumpeter (Cantwell, 2001).

Schumpeter described innovation as the changing of processes that was both structured and historical. These historical events and processes could change by innovation processes that he divided into four main processes: (1) Invention, (2) Innovation, (3) Diffusion and (4)

Imitation (Śledzik, 2013). But Schumpeter’s view, in his Innovation Theory divides an Innovation into five main types that are the most prolific ones. First type of Innovation (1) described the launch of new product and/or new specifications of an already established and existing product. Second type (2), are new innovating methods for productions or sales. Thirdly (3), opening up new possible markets. Fourth (4), having new sources of materials and-/or semi-finished goods. Fifth (5) and lastly, the creation of new industry structures, that can destruct or create new position of monopoly (Liere-Netheler et al., 2018; Śledzik, 2013). By gaining some understanding of Schumpeter’s views and contributions, together with contribution from Everett M. Roger’s to Innovation Theory, increases the understanding of how Industrial IoT fits in the context of innovation, since the technology itself are a mixture of innovation types because of the interconnectivity between multiple domains and

stakeholder (Hartmann & Halecker, 2015).

2.1.2 Adoption Theory

As already mentioned, Everett M. Rogers has impacted research in Innovation Adoption Theory in a great way, with his contributions in this matter. Further research has revealed that innovations are implemented or embraced by organizations in either two ways: generated within an organization internally or by adopting the new innovation from external

environments (Damanpour & Gopalakrishnan, 1998). The fundamental aspect for adopting new technology is as Roger’s describes:

“Potential adopters want to know the degree to which a new idea is better than an existing

one” (Rogers, 2003, p.12)

As a major contributor to Innovation Adoption Theory, Rogers (2003) is describing five characteristics, which affects an organization’s decision-making in terms of both adopting new technology and innovation, and the actual rate of adoption. These five characteristics are important to take into account and are summarized in Table 1 below, as these five

Characteristic: Description:

Relative Advantage Which is considered as the new innovation being perceived as better than the previous one.

Compatibility Which is considered as the new innovation being consistent with the past experiences and existing values within a firm.

Complexity Degree of the innovation being considered and perceived as difficult use and understand by the organization, which intend to adopt a new innovation.

Trialability Degree of rapid experiment with the technology, the notion is: the easier the new innovation/technology are for trialability, the quicker adoption.

Observability Describing how hard or easily the new innovation can be communicated or observed by every stakeholder involved in interacting with the new novel innovation.

Table 1. Identified characteristics by Roger’s: Diffusion of Innovation, concerning decision-making in adopting a new innovation or not (Murray, 2009; Rogers, 2003).

Research within a study conducted by Haddud et al. (2017) identified multiple challenges and barriers for adopting IoT in Supply Chains, which is impacting the decision-making in

adopting IIoT or not. The study involved 87 participants, from 6 different continents and identified some challenges, such as: the ability to integrate multiple supply chains and their heterogenous technologies into one coherent system (Haddud et al., 2017). The reason is that oftentimes production and supply chains have multiple independent systems operating

(Sadeghi et al., 2015). Second most difficulty with adopting IoT into production, was the lack of communication standard protocols for IoT smart devices (Haddud et al., 2017).

As challenges are identified in Haddud et al. (2017), Liere-Netheler et al. (2018) discusses some drivers of digital transformation for manufacturing firms, collected from 16 semi-structured interviews. This study concluded that there is mainly within 12 areas, which drives manufacturing firms to adopt new technologies, some of those drivers were within Process

Improvement and Workplace Improvement, but also within Cost Reduction, Management Support and within Supply Chain (Liere-Netheler et al., 2018).

2.2 Digital Transformation

The enablement during the 1960s, by using Information Technology (IT) and Information Systems (IS) to enhance business value, has been rooting itself as an indispensable factor for organization (Heilig et al., 2017; Ślusarczyk, 2018). As digital transformation within

organizations have is phenomenon’s as globalization, that are driving companies and

organizations to an even more competition between multiple actors. As competition hardens internationally and domestically, together with more demanding customers, fast and radical technological development, traditional value chains and proposition can be argued, are coming under attack from this new era of transformational- and digitalization change (Reis et al., 2018).

Much research has been done on the impact digital transformation has on businesses and that the change in business models are becoming one of the most clearly stated transformational changes in organizations (Hartmann & Halecker, 2015). However, there is a lack of evidence and research within the post-era of adopting new technology and fully integrating emerging technologies into the “DNA” of business models (Reis et al., 2018). Frameworks have been developed to integrate digital transformation into business strategies and business models, examples of such frameworks are Digital Transformation Framework (DTF) (Hess et al., 2016; Matt et al., 2015). These big changes that are being discussed in today’s industries and society, are driven by higher demands in terms of organizations being more dynamic,

customer-oriented and having a higher transformability. These ever-changing markets drive organizations to match their surroundings with their own capabilities, where one reason is out-perform their competitors:

“The effectiveness of the organization, its potential for accumulating resources, is assumed to

be a function of matching the characteristics of the environment with the capabilities of the organization” (Håkansson & Snehota, 2006, p.258).

This is creating a liability on organizations to shorten the product creation process to production, together with decreasing manufacturing costs. Being flexible and agile in their approach to market changes, are key in meeting future demands of higher product

sophistication and customer-specific products (Bauer et al., 2015). That is why organizations are looking to new merging technologies and transforming their businesses with for example, utilizing IIoT. Old approaches that brought previous success to organizations are now getting outdated (Sebastian et al., 2017) and the traditional approaches are needed to look for further adjustment of transformation, to stay competitive within their respective markets.

organization through innovations, firms can produce smaller batches of stock, with a more complex product specification (Liere-Netheler et al., 2018), by adopting emerging

technologies such as Industrial IoT. This driver of more complex products and in result, more complex manufacturing processes, are risen from more demanding customer-specific

products (Bauer et al., 2015).

Figure 2. Digital Transformation and it’s relationship and dependency on Digitalization and Digital Innovation (Osmundsen et al., 2018, p. 2).

When digital transformation is discussed within businesses and its impacts on business strategies, it’s clear that digital transformation is penetrating every aspects of business strategy and structure within an organization (Schwertner, 2017). Financial aspects are both the main driver and an inhibitory element of digital transformation, since increased

monetization for the organization and the cost of transforming the business are both needed to take into account. Since digital transformation is affecting every aspect of business, it’s mainly four dimensions DTF (Digital Transformation Framework) emphasizes on, when adopting new technology: Changes in the Value Creation, Use of Technology, Structural changes and the financial aspects. These aspects are permeating both operational strategies and functional strategies (Hess et al., 2016; Matt et al., 2015). Main impacts of Digital transformation on businesses has been widely covered in research, and there are three main areas that needs to be explained and investigated further, first (1) is the impact on customers, second (2) is organizational structure and internal business processes and ultimately, third (3) is impact on business models.

2.2.1 Impact on Customers

Important organizational aspects to take into account, is whom the source of income is for the organizations. Hence, we find the concepts of market-strategies of organizations and the distinction between Business-to-Business (B2B) and Business-to-Customer (B2C). The difference here is that B2B is involved in transactions of services and products with other organizations. B2C is the involvement of transaction of services and products with the consumer-market (Jewels & Timbrell, 2001). The impact of DT on customers in this section is referred to as B2C organizations. So, from a managerial perspective, a strong customer experience and customer impact is one of the leading agendas for organizations today, since customer loyalty and satisfaction is one of the key elements in retaining business

as, improving the seamless information flows between multiple digital ecosystems (DE) for better collaboration. The way digital transformation in organizations impacts customers is mainly by creating new valuable services and products, that customers want to pay for, even if existing models of customer interactions and engagement are present in today’s society (Piccinini et al., 2015). The possibility of making operational transformations and increasing efficiency, can also result in impacting customers in terms of less monetary payments for them to make, for a certain service or product (Morakanyane et al., 2017). As barriers between organizations, customers and “things” are getting more diffuse and torn down, this presents customers with a more personalized experience and given the customers the ability to demand more customer specific-products as a result (Bauer et al., 2015; Schwertner, 2017).

2.2.2 Impact on Organizational Structures, Processes and Routines

As digital transformation is being the option to meet future business requirements and opportunities, the challenges on the managerial level are increasing the need of

knowledgeable “know-how” about multiple technologies. The possibility of having a more flexible organization is providing the option of distancing organizations from more traditional constraints (Loebbecke & Picot, 2015; Piccinini et al., 2015). As the exploitation of new technology penetrates many areas of an organization and its structures or architectures, the resulting outcome can be new set-ups of organizational structures, for fitting and making room for the new technology in the corporation (Matt et al., 2015). The utilization and implementation of new technologies, such as IIoT, gives organizations the option to interact with customers in a more flexible manner. Loebbecke & Picot (2015) provides one good example of such flexibility, which utilizes data information for benefitting from emerging technologies. That example is Walmart, that used data from customer buying

behavior/patterns and online searches, together with weather reports, to adjust and optimize stocks and stock planning for an eventual hurricane (Bauer et al., 2015; Liere-Netheler et al., 2018; Loebbecke & Picot, 2015).

Further impacts DT can have on organizations internal processes according to Heilig et al. (2017), are the migration from individual actors having process control, to a more centralized body and having more agile logistical capabilities, in terms of “just-in-time” responsiveness to any errors or changes, that are derived from the data collected from available sensors (Heilig et al., 2017). Speaking of organizational routines, which is the tasks within organizations with a more repetitive character of interdependent actions. This effect and impact DT are having on internal business routines, are rooted in top managements involvement in changing and aligning themselves with this transformation (Teece, 2012; Vial, 2019). These repetitive characters of interdependent routines also pose a barrier for Industry 4.0. Which is in context, also a liability on talent acquisition and putting high

mainly targeted for implementation of technologies such as IIoT. As routine tasks in

organizations are of repetitive character, the automation of tasks is where the transformation is mainly targeted and where the major changes are coming. Workers with low level routine tasks can- and are being conducted by automated procedures and digital routines (Satoglu et al., 2018).

2.2.3 Impact on Business Models

Digital Transformation has the ability to change the organization on so many different levels, since the digital technology can both be exploited to improve current and existing processes, and also potentially transforming business models (Berghaus & Back, 2016; Matt et al., 2015). As businesses need the transformation to fit with new digital technologies, some benefits connected to altering organizations business models are decreasing transactional costs, in for example, information gathering from channels such as controlling activities and communication (Loebbecke & Picot, 2015). As the technology is a rapid changing domain, some business leaders are having difficulties with keeping paced with this rapid change within the technology-domain. However, this rapid change is still important to embrace and think about, as being rapid to market response, developing capabilities and implementing digital transformation into business models are important aspects for business leaders to consider (Hess et al., 2016). By pursuing the implementation of DT and integrating new technologies into newly developed business models are rather complex, but can follow roadmaps, such as the one Schallmo et al. (2017) is presenting:

Figure 3. Suggested roadmap for implementing Digital Transformation into business models (Schallmo et al., 2017, p. 8).

(Morakanyane et al., 2017). But this transformation of business models also becomes one of the main challenges with implementing emerging technologies (Haddud et al., 2017). While organizations pursue often times higher productivity and higher sales rates, new innovations provide new generated values that can provide higher overall business performance, as a result new business models emerge, with the help of digital transformation (Matt et al., 2015). These challenges that emerge with new innovations entering the scene, often times it is mature and large organizations where implementation of new business model are being perceived as a daunting task (Hartmann & Halecker, 2015). There needs to be an

understanding, that Business Models are the very heart of organizations, that are put in place to define where firms’ revenues and profits will come from. The goal and activities defined in business models are put in place for mainly minimizing firms costs and maximizing their revenues (Ju et al., 2016). As business models are being the very blood-vessel of managing organizations and strategically positioning themselves within a specific market, what needs to be understood is how novel business models and digital transformation is related for

increasing or changing a firms’ value generating activities and value creation. “From a

business perspective, the use of new technologies often implies changes in value creation”

(Matt et al., 2015, p. 340).

2.3 Industry 4.0 and Servitization

Industry 4.0 is defined and identified as an interdisciplinary concept, with no clear distinction between the technologies which is included in term. The specific term “Industry 4.0” refers to the planned 4th revolution, with huge potential within manufacturing, and creating digital value chains for organizations (Oesterreich & Teuteberg, 2016). There are many definitions available for Industry 4.0, one of those definitions are “The integration of Information and

communication technology into the industrial environment” (Horváth & Szabó, 2019, p.120).

Some other technologies that are included in this concept of Industry 4.0 (4IR) were for example: Artificial Intelligence (AI), Big Data, Cloud Computing and Internet of Things (IoT) (Hofmann et al., 2019; Koh et al., 2019). These technologies are able to completely disrupt the current way of conducting business, managing organizations, developing business models, and the list continues to go on. Value generation is the content of having many activities that an organization can decide to take. But there are three main important

assumptions that need to be questioned, in order to understand how value is being realized in this context.

(Hartmann & Halecker, 2015; Weyer et al., 2015), Industrial IoT (IIoT), Big Data, Artificial Intelligence (AI) and Cloud Computing & Manufacturing (Hofmann et al., 2019; Koh et al., 2019). What is important to acknowledge in the technical aspect, is that the Cyber Physical System (CPS) is considered as the enabler of IIoT by being a collection of sensors, data processing units and actuators. CPS is the main technical core and “skeleton” of IIoT, and when the CPS is integrated into manufacturing, it is also known by as: Cyber Physical Production System (CPPS) (Kiel et al., 2017). The concept of, and the new emerging technologies within the term “Industry 4.0” are generally accepted. However, there is some discourses which argues, that Industry 4.0 are an entire concept and procedure of how to share, use, organize and acquire data as an resources to enable more effective, faster, cheaper and more sustainable product and service deliveries (Hofmann et al., 2019; Koh et al., 2019). When researchers refer to servitization in this context, the main implication and definition of the term is the transformation of core business value proposition, enabled by using new technology. The notion is that manufacturers are offering more developed service propositions to its customers and stakeholder (Paschou et al., 2018).

This trend of “industry 4.0” are enabling automated creation of goods and different services, together with the transformation of how businesses supply and deliver goods and services, without any human input. This transition are happening right now in today’s society, where the central movement are towards automation and different types of data exchange in manufacturing processes and technologies (Hofmann et al., 2019). Industries see 4IR as an opportunity and not as a risk towards their own companies and businesses (US= 90%, Germany=92% and Japan=78%). In a survey presented by Ślusarczyk (2018) shows that 80 % of the surveyed organizations are considering 4IR as being significant and important to utilize and take into account (Ślusarczyk, 2018). These new technologies are connecting multiple devices into one solution, which operates autonomously. These components are also enabling communication and analysis to further develop intelligent decision-making for different businesses (Hofmann et al., 2019). The digital transformation and the possibilities that are becoming apparent with these emerging technologies can affect businesses decision and business models in multiple areas (Berghaus & Back, 2016; Zott & Amit, 2017). Where research topics can be within for example: Supply Chain strategies, Customer Value

Figure 4. Concept and application of IIoT between physical and digital systems (PLM refers to Product Lifecycle Management) (Jeschke et al., 2017, p. 6).

Furthermore, Industry 4.0 can be divided into three main paradigms, where smart products (1) refers to machines and objects with that are being equipped with microchips and sensors that are connected to the internet and being controlled by computer-software. These smart products enable the possibility of storing and utilizing operational data and requirements completely independently, for tasks such as active data utilizing for when to produce, where to produce and what the possible parameters could be to adopt in the product manufacturing process. The second (2) paradigm are Smart Machines, which are devices equipped with cognitive computing and machine-to-machine (M2M) technologies, for the utilization of machines independent problem-solving, reason and decision-making for taking action in the production line. The third (3) paradigm includes augmented operators (Koh et al., 2019; Pticek et al., 2016; Romero et al., 2016; Weyer et al., 2015), whom are the ones working with the digital tools, real-time sensor data and information transfer/monitor to the physical world, enabling the maintenance for Smart Factory environments (Romero et al., 2016). This

increases the modulatory and flexibility for production worker, that are working together with these emerging technologies (Koh et al., 2019; Pticek et al., 2016; Weyer et al., 2015). The entire setup to utilize the technology that Industrial IoT enables, has an multilayered structure (Fig. 5), where Wireless Sensor Networks (WSN) are the main data gathering input from the network of active sensors that are operating. Next layer of an entire concept of IoT

Figure 5. Multilayered approach to implementation and utilization of Industrial IoT (Pticek et al., 2016, p. 6).

These benefits by using and implementing the technology also strengthens industries in resource consumption and by the possibility of optimizing resources. However, there are still some identified uncertainties and challenges within the field of Industrial IoT and the other emerging technologies within Industry 4.0. Some of those challenges are standards for using the connectivity and security that IoT brings, where multiple stakeholders can agree upon those standardized views and approaches. These are vital for securing the future of IoT implemented within the industry concerning the collaboration and communication between IoT technologies, instead of having multiple standalone IoT-ecosystems (Mourtzis et al., 2016). While estimate are predicted that around 28 billion IoT-connected smart devices will be connected worldwide by the year 2020- 2021 (EY, 2016; Haddud et al., 2017). Adoption of the technology is increasing businesses complexity in terms of integrating big data input into their business plan. How the value of businesses and the possible development of business plans are re-shaping organizations and their relationship to customers are in many ways complex and brings benefits with it. By utilizing IoT into operations, the possibility of predicting and leveraging data input for alerting organizations of customer feedback, while using smart products that are connected to the emerging smart supply chain and industrial ecosystem (EY, 2016).

As the benefits are accounted as creating better communication and collaboration amongst multiple actors, optimizing operations, using complex autonomous systems and sensor-driven decision-making, the technology is still considered to bring a huge impact in the future

ecosystems (Ju et al., 2016; Liere-Netheler et al., 2018; Schallmo et al., 2017). As the technology continues to develop, multiple value propositions are becoming evident as

beneficial for adopting Industrial IoT. Information in itself, are vital for organizations as data input are a major source of value proposition and value creation. Those value propositions are helpful for organizations for gaining and understanding consumer needs and the analysis of the data input makes it possible to generate new value propositions as a result. Those value propositions which is driving the development of new business models are mainly improved and increased business performance, convenience and customization of organizations services and products (Ju et al., 2016). Examples of such business models which can be argued to fit the desire of higher resource productivity, major cost saving and reducing the environmental impact of manufacturing firms are Circular Business Models (CBM) (Linder & Williander, 2017).

Some scholars have been emphasizing the importance and the need for increased and

developed service orientations for organizations. As the time for only focusing and caring on their own products and goods, are over. In present society firms are being required to focus on and adjust value propositions to a consolidation between products and services. These services can provide the opportunity for new business models, which in return can increase both profits and revenues, by moving an organizations’ focus from products, to selling services instead (Fonseca, 2018).

2.4 Value Creation

In Industry 4.0 technologies, digital connections for a more efficient value creation in industries is being created (Kiel et al., 2017). Scientific literature makes the distinction in value creation, in three main domains: Firm value creation, value co-creation and customer value creation, as these will be analyzed throughout this section.

2.4.1 Firm Value Creation

The more traditional approach to value creation and its process, has been in organizations internal possession, meaning: the firms has been seen as the creator of value, showing up in their end products. Literature and research has also been mainly focusing on developing and addressing frameworks for internal organizations value creation (Gummerus, 2013). As research and literature has been mainly focusing on the technical aspects of IIoT, it is still considered that the application of IIoT, are convenient to address issues that organization face today: Shortening technological & innovations cycles, increased customization and

The “value” in “value creation”, in regards to what Industrial IoT actually can create in a more practical sense in production and manufacturing, is cost reduction potentials and reducing the consumption of waste and energy. As value drivers are being pursued for this degree project, IIoT are impacting business in multiple ways, these impacts and increased value drivers can be analyzed through the Triple Bottom Line (TBL), which is a framework for organizations’ to create sustainable value through the three dimensions of social,

economic and environmental aspects (Kiel et al., 2017). These value drivers are further displayed, analyzed and discussed in section. 6: Discussion.

Together with creating value for everyone involved in the production process, with creating a more flexible and agile working environment (Kiel et al., 2017). While, firms are working with the help of digital transformation and digital tools to improve and create value

internally, there is no going away from how firms today create and generate value through, and with the help of all stakeholders: “The digital way of value generation is marked by an

in-depth integration of customers and suppliers” (Liere-Netheler et al., 2018, p.3928). As

where the value for organizations are being discussed by scholars, what’s clear is that the adoption of such technologies, will require interdisciplinary knowledgeable workers. As the physical and cyber world are merging closer together, workers whom are working close to such technologies, will need further education, which will be an valuable asset for

organizations (Koh et al., 2019). Kiel et al. (2017) is providing an interesting overview of which aspects IIoT provide value (Fig. 6), and comprises of insights from 46

Figure 6. Perceived benefits and value in using IIoT across multiple industries (Kiel et al., 2017, p. 19)

2.4.2 Value Co-creation

This also pose challenges in developing and utilizing digitalization and it’s capabilities, to create value for multiple actors (Lenka et al., 2017; Paschou et al., 2018).

2.4.3 Customer Value Creation

Customers becomes more included in the value creation process, because organizations are developing deeper relationships with its customers (Paschou et al., 2018). Where the including of customers and users of a certain services and-/or product can be a part of the value creation chain. As organization’s are pursuing the goal of maximizing customer satisfaction and value, for gaining a good relationship with their own customers. The goal of satisfying customers and partners, and ultimately all stakeholders, are rooted deep in gaining loyalty amongst stakeholders and having continuous generating business value (Gummerus, 2013). What seems to happen is that the customer journey is increasing in complexity for organizations to meet their respective customers. How this difficulty presents itself, is through the way customers are digitally interacting with digitals tools and media in present society (Lemon & Verhoef, 2016). This way of embracing digitalization and transformation develops and extends many business value chains further. This embracement of new

technologies, can result in new segments, which in the end, increases new services and products that consumers can reap the benefits from, and increasing customers value generation (Schwertner, 2017).

While, value creation is generating values in multiple aspects for organizations, Industrial IoT is changing the way value is created and changing the industry landscape and dynamics of industries. Currently, in industries which are generating value creating activities because of the implementation of IoT into operations, can be seen in industries such as, Agriculture (Smart Agriculture), with the usage of drones and advanced analysis for seed planting patterns and producing 3-D maps. Or, in energy consumption (Smart city), where energy corporations are exploring the opportunities with IoT for new value creation in smart city development and transportation. Wearable technologies (Smart Life), is also one area where IoT are creating increased value creation, mainly though social and personal everyday usage, where for example; Tennis players utilize IoT within their rackets for learning and improving striking patterns from the information obtained from data input (Guo et al., 2013; Jeschke et al., 2017; Nobre & Tavares, 2017; Ustundag & Cevikcan, 2018).

Where this project will position itself from, is based on the critical literature review and the significant shortage of research within the cases of Swedish production focused

organizations, especially in the Mälardalen-area, and their possible adoption of new emerging technologies, such as Service/Industry 4.0 and what value the technology can contribute with for increased productivity, efficiency and reliability.

2.5 Summary and Process of Literature Review

interconnected with every aspect of a company. Therefore, as Industrial IoT has this high degree of complexity, multiple topics were of interest and needed for getting a clear

understanding of the topic. These areas of interest and its complementary literature is within: Digital Transformation, Value Creation Theory, Innovation Adoption Theory and about the technology itself, Servitization and Industry 4.0. The approach of reading and processing the material that are present within these four (4) main areas, were approached critically, and with the consideration of not having Conformation Bias (Gioia et al., 2013) when the journals where encountered, read and analyzed. The method and process of the literature review, were conducted according to these four main iterative aspects:

First, different journals were approached critically and been trying to avoid Conformation

bias (Gioia et al., 2013), together with going back and forth between what the study’s research questions is, and what topics the research questions needs to be drawn upon. The search engines that has been extensively used, were Google Scholar and the usage of Uppsala University Library, for finding the relevant scientific journals. In this way, the approach had to get a broad overview of the topic initially, where searches for general key-words were done: IoT, Industrial IoT, Digital Transformation, Value creation, Industry/Service 4.0, Digitalization and Adoption of Innovation.

Second, after gaining a broad understanding and relevance within the subject of Industrial

IoT and its success factors, drivers and impacts on how organizations decide whether or not to adopt an emerging technology and digitally transforming their operations. There was themes and connections found in the scientific journals, which is the backbone of the literature review in this study, which is being investigated.

Third, based on the findings and realizations from reading scientific journals within these

main areas of interest that the topic is investigating, there was a constant change and feedback loop of the Research questions and the scope of the project. As of the findings from the literature review, the problematization further emerged on progress of time.

Fourth, during the actual writing of the findings- and basis from the literature review, the

researcher wrote the text according to an intro, main body and one conclusive text. From this basis of information, the topic could position itself based on the literature review and found out gaps in the scientific area of identifying key value creation and how mature Production focused companies in Mälardalen, Sweden are for Digital transformation and adoption of new emerging technology, such as Industrial IoT.

Figure 7. The iterative and flexible approach to the literature review, that the researcher of this study worked according to.

These four domains of literature and theory that has been through literature reviewing are together bringing a drive for creating new concepts, themes and dimensions, as Grounded Theory is the trade-off between empirical data and literature. This trade-off is extensively explained in the next chapter (Section. 3: Methodological approach). Gioia et al. (2013) state that, as data are being collected and generated the frame of references are not entirely set, as grounded theory has a semi-ignorance towards previous literature. The emphasis on collected empirical data are the driving analysis and therefore, according to Grounded Theory, this distinction between previous literature and the driving of analysis are not set. On the other hand, the literature are used for consulting the resulting concepts and themes that has emerged from the iteration between gathered data and previous literature. This consultation with previous literature, ensures that the research has found concepts, themes and aggregated dimensions that has precedents, or to see if the research has discovered any new concepts within this context. This approach towards previous literature are according to Grounded Theory, not pedantic and therefore “we are freed from the chains of being pedantic and

thorough” in that sense (Gioia et al., 2013, p. 23). But the main emphasis and importance in

the context of grounded theory, to ensure a thorough and clear methodology-section, which will be encountered and further explained in the next chapter.

Research Question

Topics and Areas of Interest

New Findings & Relevance Narrowing down the

scope of the study Revision of Research

3. Methodological approach

The methodology and the process of this study mainly found its inspiration from two major sources. Bryman & Bell (2011) is a source for mainly general approaches and information on how to plan and conduct a research study and interview. Gioia et al. (2013) is the main source of inspiration and analysis framework for the thematic analysis. Both the research methodology, data collection and ethical considerations is described in this section. The research framework that this project is working according to is Grounded Theory.

3.1 Research Context and Design

The research context of for this thesis to cover value generation for production firms in Mälardalen-area in Sweden. The background of this context is that many firms in the area are at different stages on their digitalization journey. This sample of such firms becomes a good empirical fit for this thesis. The unit of analysis will be to capture the process of value creation as phenomena. But more specifically, when it comes to the research design and approach, the thesis adopts a qualitative method as described by Bryman & Bell (2011) in the overall structure and procedure of a qualitative analysis. “Qualitative research can be

constructed as a research strategy that usually emphasizes words rather than quantification in the collection and analysis of data and that: predominately emphasizes an inductive approach to the relationship between theory and research” (Alan Bryman & Emma Bell,

2011, p. 27)

The study pursues a Grounded Theory approach, which is the generating and creation of based on empirical data. The process of the study adopts a typical grounded theory-approach which is a continuous trade-off between data and theory, in this sense: the whole process is iterative in its nature, hence, the study follows an inductive analysis approach. The outcome and result of Grounded Theory is mainly new concepts and categories (Gioia et al. (2013) calls these aspects: 1st order Concepts and 2nd order Themes respectively, which this study will also label them as) (Alan Bryman & Emma Bell, 2011; Gioia et al., 2013).

knowledgeable in the topic of Industrial IoT, they can come prepared and well-informed about the topic of interest (Ibid).

The collection and external references are used in this context to get a well-rounded

foundational understanding of the topic and what the study intend to investigate. Therefore, there has been mainly the collection of qualitative journals and texts in four areas: Adoption Theory, Servitization and Industry 4.0, Digital Transformation and Value Creation Theory. As these four topics are highly related and interconnected, in terms of emerging technologies and when it comes to adopting and creating value based on digital transformation of an organization, and its implementation of IIoT. The literature review is reflecting the data collection of which the thesis builds its foundation of understanding.

The design framework in this study, will be following Bryman and Bell (2011) example of how to conduct an qualitative study, which this thesis is pursuing. There is certain stages and steps that are being presented by Bryman & Bell (2011), which follow the process of how to conduct a qualitative study accordingly: The creation of a research questions, which this study will pursue and try to answer, second stage in this process is selecting relevant resources which is relevant to have, as a foundation of understanding. Third stage is the collecting of primary data from stakeholders in the topic of interest. Fourth stage in the process is the interpretation of data and this is where the process is iterative, in terms of gaining more data input and tighter specification and development of the research questions. As the last stage, there will be finding conclusions and summarizing up the main conclusions. This is the general pathway that the study will pursue (Fig. 8). Furthermore, the research into the topic of interest will be used as a basis for Avalon Innovation for decision-making, better consulting and understanding of the industry and technology in terms of the digitalization, drivers and what value by adopting Industrial IoT could be, for companies in the Mälardalen-area, Sweden.

3.2 Data Collection

The data collection is drawn from five interviews, and are the main source of insights and access to multiple industry perspectives. In this section, the sampling approach, the process of the literature review and the process of the interviews will be presented below.

3.2.1 Sampling

Two sources of data were implemented, first was sampling the subjects to interview, second was on sampling key publications on Industry 4.0 value creation through key-word search, the latter is explained below. The sampling approach that this project is pursuing and using, is the form of purposive sampling. The participants that are included in the project for data collection and interviews are not being chosen on a random basis, hence the sampling

approach is a non-probability sampling approach. Since, purposive sampling have the goal of using data and gain knowledge from multiple cases (meaning organizations and employees) in a strategic way, having a variety of key characteristics such as positions within companies and-/or amount of years in the industry, is to ensure a wide variety of perspectives. Within purposive sampling, there is a form called Snowball Sampling, which involves finding and gaining access to participants based on recommendation from an initial contact from a small group of people. By using the contacts and access to a small group of relevant people from the project’s perspective, new units of participants could be accessed. For this study, since the topic is specifically investigating Industrial IoT and new emerging technologies, the

researcher of this project, got access to an initial group of interviewees and participants who have been at different levels of adopting Industry 4.0 in their value chains. Through previous meetings with employees and managers, and thought the supervisor’s contacts, subjects within the production-industry were identified and contacted which resulted in establishing relevant participants to be interviewed and involved in this project.

3.2.2 Interviewing

A total of 5-in depth interviews were conducted thought Videocall-interviews and face-to-face meetings (F2F), mainly by voice-recording the meeting and the conversation for later analysis. The process of the interview guide (See Appendix A), which includes all necessary information for the participants to make an Free & Informed Consent (Section 3.4 Ethical

Considerations) whether they want to participate or not. The interview guide also included all

questions that the project intend to investigate. The interview guide was created through the findings from the literature review, according to this iterative process: (1) During the literature review, themes of interconnectivity were identified within the research conducted by previous researchers within IoT. (2) Parallel with the literature review, there was multiple reflective and revisits to the actual Research Questions of this study, to ensure critical

process, the interview questions slowly emerged to be accounted for, when the researcher were trying to answer the projects research questions. (6) These interview questions then changed multiple times, during the continuous insights gained from conducting and meeting further participants of this study. The names of the participants has been fictional to mask their real names, since they took the decision of staying anonymous throughout the interview-process. The only participant that agreed on using his real name was Jonas Gustavsson.

The actual questions were altered in tandem with conducting the interviews, since the

participants were viewed as “knowledgeable agents”, whom are very well aware of their own actions and why they are going what they are doing (Gioia et al., 2013). Therefore, all of these participants gave the researcher a well-rounded data collection, from multiple perspectives to take into account.

Type

Organizational

Level

Years of

experience

Representative

Interview-duration

F2F, on site Production Worker 2 years Anders 58 min

F2F, on site IT Strategy Consultant

3 years Martin 39 min

F2F, on site Production Specialist

17 years Jonas Gustavsson 56 min

Video-call Quality Assurance

Manager

1 year Gustav 31 min

Video-call Manager in

Industrial Systems

14 years Karin 42 min

Table 2. Background on the participants and general information on setting and timeframe regarding the interviews.

3.2.3 Documents

Through key word search such five different sources was deemed relevant which is seen in Table. 3, for having multiple sources of evidence to have a comparison between the