The Influence of Industry 4.0
on Globalisation Strategies
of Multinational Enterprises
MASTER THESIS WITHIN: Business Administration NUMBER OF CREDITS: 30 credits
PROGRAMME OF STUDY: International Logistics and Supply Chain Management AUTHORS: Minna Sivertsson and Julian-Marcell Utz
TUTOR:Imoh Antai
JÖNKÖPING May 2021
A Qualitative Study of MNEs and Their Business Decisions
Regarding Offshoring and Reshoring Strategies
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Master Thesis in Business Administration
Title: The Influence of Industry 4.0 on Globalisation Strategies of Multinational Enterprises; A Qualitative Study of MNEs and Their Business Decisions Regarding Offshoring and Reshoring Strategies
Authors: Minna Sivertsson and Julian-Marcell Utz Tutor: Imoh Antai
Date: 2021-05-24
Key terms: Industry 4.0, Globalisation, Offshoring, Reshoring, Manufacturing Industry, MNEs
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Abstract
Background: Historically, industrial revolutions enabled societal shifts in conjunction with
technological advancements. The currently ongoing industrial revolution is the fourth, namely Industry 4.0. Industry 4.0 related technologies require a high level of integration to improve processes and yield more efficient flows of information, physical and financial assets. This integration happens within and between GPNs, which comprise of globally fragmented points of economic activities, including manufacturing activities. New technologies advance these GPNs, causing a qualitative shift, meaning where and how production and consumption activities are changing. The idea of Industry 4.0 is still within its infancy, where the study of Industry 4.0 drivers and barriers for MNEs remains unexplored. Industry 4.0 is in the process of transforming how industries operate with access to new advanced technologies. These technologies can affect GPNs and potentially influence related business decisions regarding offshoring and reshoring decisions in their globalisation strategies. In relation to Industry 4.0 drivers and barriers, this was already investigated for SMEs, leading to the purpose of this study.
Purpose: This study investigates the influence of Industry 4.0 on the motivation of MNEs
regarding offshoring and reshoring strategies within a global supply chain context.
Method: This is a qualitative study, that explores this field through the experiences of industry
experts of MNEs. Hence, the study takes a relativistic and social constructionist stance in terms of ontology and epistemology, to inquire into this topic through gathering and comparing in depth experiences. Furthermore, gathered data was analysed through a thematic analysis approach.
Conclusion: The result of the study shows that Industry 4.0 is emerging as a comprehensive
concept that goes beyond just the technologies, drivers, and barriers. Based on our findings, this is referred to as a value network, replacing the contemporary view on GPNs. Hence, this value network is emerging as its own globalisation strategy that directly influences the motivations for MNEs to offshore and reshore.
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Acknowledgements
The completion of this thesis and our Masters program at Jönköping International Business School (JIBS) could not have been possible without the contribution of numerous kind and knowledgeable people we would like to recognize. First, we want to direct a great thank you
to all participants that gave us their valuable time and insights despite their busy schedules, without you we would not have been able to complete this thesis. It has truly been a great
pleasure to take part of your experiences and insights.
We would also like to show our gratitude to our supervisor Imoh Antai and our seminar team who have helped us continuously throughout this whole process through shown support and
valuable feedback that have improved the quality of our work.
Lastly, we want to take this opportunity to thank JIBS and our lecturers for the last two years, who even in the middle of a pandemic has provided us with an interesting and knowledgeable experience. This thesis has been an inspiring journey and we are grateful for the experience
and all the great people we have had the pleasure to interact with.
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Table of Contents
1. Introduction ... 1
1.1 Background ... 1
1.2 Problem Description... 2
1.3 Purpose and Research Questions ... 3
1.4 Scope and Delimitations ... 4
1.5 Outline ... 4
2. Theoretical Framework ... 6
2.1 Global Production Networks ... 6
2.1.1 Challenges ... 6
2.1.2 Efficiency and Costs ... 7
2.1.3 Quality and Disruptions ... 7
2.2 The Role of FDI in GPNs ... 8
2.3 Globalisation Strategies ... 9 2.3.1 Motivations to Offshore ... 9 2.3.2 Motivations to Reshore ... 10 2.4 Industry 4.0 ... 12 2.5 Industry 4.0 Technologies ... 13 2.5.1 Internet of Things ... 14 2.5.2 Cloud Technologies ... 15
2.5.3 Big Data Analytics ... 15
2.5.4 Additive Manufacturing ... 16
2.5.5 Augmented Reality and Simulation ... 16
2.5.6 Automation and Robots ... 17
2.5.7 Cybersecurity ... 17
2.5.8 Horizontal and Vertical Integration ... 18
2.6 Industry 4.0 Drivers and Barriers ... 18
2.7 Framework ... 20
3. Methodology ... 22
3.1 Research Philosophy ... 22 3.2 Research Approach ... 23 3.3 Research Design ... 24 3.3.1 Thematic Analysis ... 253.3.2 Multiple Case Study ... 26
3.3.3 Case Selection ... 26 3.3.4 Literature Search ... 27 3.4 Data Collection... 28 3.4.1 Interviews ... 28 3.5 Quality Assurance ... 29 3.6 Research Ethics ... 31
4. Empirical Findings ... 32
4.1 Case Company A ... 32 4.2 Case Company B ... 34 4.3 Case Company C ... 37 4.4 Case Company D ... 39v
4.5 Case Company E ... 42
4.6 Case Company F ... 45
5. Analysis and Discussion ... 48
5.1 Case Company A Analysis ... 48
5.1.1 Competitive Edge ... 48
5.2 Case Company B Analysis ... 50
5.2.1 Competitive Edge ... 50
5.2.2 Location Attributes ... 51
5.3 Case Company C Analysis ... 52
5.3.1 Economic Efficiency ... 52
5.4 Case Company D Analysis ... 54
5.4.1 Competitive Edge ... 55
5.4.2 Location Attributes ... 55
5.5 Case Company E Analysis ... 56
5.5.1 Productivity ... 56
5.6 Case Company F Analysis ... 58
5.6.1 Leading Edge ... 59
5.6.2 Economic Efficiency ... 60
5.7 Research Question 1 Discussion ... 61
5.7.1 Industry 4.0 Drivers ... 61
5.7.2 Industry 4.0 Barriers ... 63
5.8 Research Question 2 Discussion ... 65
5.8.1 Offshoring and Reshoring Strategies of MNEs in Practice ... 66
5.8.2 Industry 4.0 - Value Network ... 68
6. Conclusion ... 71
6.1 Implications ... 71
6.2 Limitations and Future Research ... 72
7. Reference List ... 74
8. Appendices ... 80
Appendix A - Timeline ... 80
Appendix B - Literature Search ... 81
Appendix C - Topic Guide ... 83
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Figure
Figure 1; Thesis Outline ... 4
Figure 2; Framework ... 21
Figure 3; Matrix of Philosophies and Approaches ... 23
Figure 4; Research Design ... 24
Figure 5; Case A Analysis ... 48
Figure 6; Case B Analysis ... 50
Figure 7; Case C Analysis ... 52
Figure 8; Case D Analysis ... 54
Figure 9; Case E Analysis ... 56
Figure 10; Case F Analysis ... 58
Figure 11; Revised Framework ... 69
Figure 12; Relation between Industry 4.0 Drivers and Globalisation Motivations.... 70
Figure 13; Relation between Industry 4.0 Barriers and Globalisation Motivations ... 70
Tables
Table 1; Motivations to Offshore ... 9Table 2; Motivations to Reshore ... 11
Table 3; Industry 4.0 Technologies ... 14
Table 4; Industry 4.0 Drivers and Barriers from Literature ... 19
Table 5; Thesis Interviews ... 29
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1. Introduction
__________________________________________________________________________
This opening chapter gives a brief introduction to the study through a background, problem description, purpose and research questions, scope and delimitations, and lastly an outline.
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1.1
Background
Industrial revolutions have had an immense impact on the civilisation throughout history (Suraj Kumar, 2019), both for production methods and processes, but also society as a whole (Dicken, 2015). The first industrial revolution led to mechanical manufacturing, the second used electric power to enable mass production, while the third helped automate manufacturing (Suraj Kumar, 2019). The fourth industrial revolution is the current one, also called Industry 4.0. Each industrial revolution enabled societal shifts in accordance with associated technological advancements (Dicken, 2015). Throughout the history of industrial revolutions, technology contributed to converging space and time by overcoming space-time related obstacles. Hence, this leads to a global shift, enabling globalisation.
According to Ustundag and Cevikcan (2018), there is no universally recognized definition of the concept ‘Industry 4.0’ amongst academics, but normally includes technologies such as Internet of Things (IoT), cloud technologies, virtual and augmented reality, Big Data, and Artificial Intelligence (AI), amongst others. Industry 4.0 related technologies are termed as cyber-physical systems which are integrated into manufacturing and logistics activities (Stentoft & Rajkumar, 2020). These technologies are said to improve efficiency, adaptability and cooperation through elevated connectivity and integration (Ustundag & Cevikcan, 2018), influencing value creation, business models, downstream services and work organizations (Stentoft & Rajkumar, 2020). In addition, the mentioned technologies require a high level of integration, and can consequently lead to both cost- and time efficient competitive advantages when effectively managed (Tiwari, 2020). Taking this one step further, this level of integration within a supply chain (SC) can essentially lead to both intra- and inter-organizational improved processes, and hence lead to more efficient information, physical, and financial flows among actors.
Continuing this with a perspective of globalisation, today’s economy is increasingly integrated through complex global production networks (GPNs) and mechanisms (Dicken, 2015). Such
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integration occurs within and between these GPNs, which comprise of geographically fragmented points of economic activity, including production, distribution and consumption. Furthermore, Dicken (2015) explains that “the crucial diagnostic characteristic of a ‘global economy’, therefore, is the qualitative transformation of economic relationships across global space” (p. 6). The qualitative transformation refers mainly to where and how production, distribution and consumption activities are geographically changing, but also the nature and degree of interconnection of these economic activities. Moreover, it is not a new phenomenon that GPNs advance through new technologies which increase the speed of these interconnected economic activities, which is referred to as the economic relationships being stretched and intensified.
A key foundation of globalisation is Foreign Direct Investment (FDI), as it allows Multinational Enterprises (MNEs) to directly invest in controlling foreign production activities, which enables them to operate on a global scale (Westerberg & Andersson, 2008). FDI manifests offshoring and reshoring strategies by driving the investment decisions in foreign manufacturing activities (Lund & Steen 2020; Musteen 2016). Technology plays a role in this decision-making process (Dicken, 2015), as it enables reduced costs of manufacturing within the network (Stentoft & Rajkumar, 2020). This train of thought is further reinforced by technology being described to be a main driver in globalisation of economic activity, where technologies are described to transform and change processes (Dicken, 2015). Therefore, the use of technology has the power to influence business decisions that are primarily driven by profit.
1.2
Problem Description
The idea of Industry 4.0 is still nascent and is slowly recognized by corporations and implemented in practice (Stentoft & Rajkumar, 2020). Hence, the increasing awareness of the drivers and barriers of Industry 4.0 related technologies enables corporations to recognize their potential. This in turn may enhance corporate globalisation strategies if Industry 4.0 is implemented effectively. As mentioned earlier, this field is still in its infancy with little standards being practiced yet (Prinz et al., 2016), which contributes to the complexity of this study as corporations themselves are not yet practicing Industry 4.0 to its fullest potential. Stentoft and Rajkumar (2020) recently conducted an exploratory literature review in the field of Industry 4.0 and globalisation, in order to identify drivers and barriers for companies to implement Industry 4.0 practices. Furthermore, they investigate how the implementations of
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these integrated technologies govern corporate globalisation strategies, such as offshoring and reshoring. However, their study is limited to Small and Medium Size Enterprises (SMEs) confined within Denmark. As the authors state, some of the barriers that these companies face is the lack of financial resources, lack of manpower and more focus on operations at the expense of company development. This fact is further confirmed by Pech and Vrchota (2020), who identified that compared to large sized enterprises, SMEs have fewer financial resources, less manpower, lack of Industry 4.0 related knowledge, and insufficient skilled labour to implement Industry 4.0. Consequently, it enables large sized enterprises to realize Industry 4.0 technologies involving less obstacles.
1.3
Purpose and Research Questions
The concept of Industry 4.0 is currently part of the fourth and latest industrial revolution, transforming how industries operate with access to new advanced technology. In our current global society, these technologies can further affect companies’ GPNs and potentially influence business decisions in accordance with this phenomenon. Similar situations have been examined in relation to SMEs, however this is lacking for larger companies operating on a global scale. Accordingly, the purpose of this study is;
To investigate the influence of Industry 4.0 on the motivation of MNEs regarding offshoring and reshoring strategies within a global supply chain context.
In order to study what influence Industry 4.0 has on the decision making in MNEs, it is necessary to understand which Industry 4.0 aspects are imperative for companies this size. The first research question is therefore formulated to address this.
1. Which drivers and barriers of Industry 4.0 are most relevant for MNEs within their GPNs?
When relevant drivers and barriers have been identified, their relation to business decisions regarding offshoring and reshoring strategies needs to be established. The second research question is therefore formulated as follows.
2. How do these drivers and barriers relate to offshoring and reshoring strategies of MNEs in practice?
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The research questions will be examined through a review of literature and qualitative in-depth interviews with managers of MNEs. Gathered data is thereafter analysed and discussed to answer the purpose of the study. Hence, contributes to filling the aforementioned gap in literature. Furthermore, the study provides insights for managers of MNEs in preparation for business decisions regarding offshoring and reshoring.
1.4
Scope and Delimitations
This study investigates how Industry 4.0 motivates business decisions regarding offshoring and reshoring activities in a SC context. This study will therefore focus solely on its influence on offshoring and reshoring decisions and will disregard how Industry 4.0 directly influences other components of a business’ strategy. As this study will focus solely on offshoring and reshoring activities, the study will be limited to the perspective of the focal company as actor in the SC. Further, in accordance with the study of Stentoft and Rajkumar (2020), which as mentioned focuses on SMEs in a similar situation, we instead investigate this concept on MNEs. In Stentoft and Rajkumar (2020) study, participating companies ranged between 1-500 employees. Hence, why our samples consist of only MNEs with more than 500 employees. In addition, only companies with a base in central Europe partake in our study.
1.5
Outline
This study is divided into six different main chapters (see Figure 1), which are thereafter broken-down between subheadings for a simpler reading structure.
Figure 1; Thesis Outline
The opening chapter comprises of an introduction to the dissertation, including an overview
addressing the main topic and justification of the study. This chapter presents the purpose and research questions that the study is based on while providing a clear view of the following study format. The second chapter regards theoretical material that offers an increased understanding of the current field of study and provides a distinct background for further comprehension. The
third chapter describes the methodological research approach in detail and justifies the
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ethics and research quality. Empirical findings are presented in the fourth chapter, including brief descriptions of participants. The fifth chapter firstly analyses gathered data and later discusses this in relation to current literature. The final chapter aims to conclude the conducted research and discusses implications and future research.
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2. Theoretical Framework
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This chapter provides the reader with an extensive search of existing literature within Industry 4.0 and globalisation strategies. The literature aims to provide an increased understanding of the research topics and to act as guiding material throughout the study.
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2.1
Global Production Networks
There is extensive literature on Global Production Networks. Within this context, there is a common consensus among academics on what GPNs are and what their main purpose is in today’s global economy. GPNs are formed as corporations move their production capacities beyond their national borders, thereby globally distributing manufacturing activities (Moser et al., 2016; Prinz & Bauernhansl, 2013; Reuter et al. 2016a; Reuter et al., 2016b; Schuh et al., 2017). This is what we term as global corporations.
The motivations behind this shift in manufacturing activities may best be explained by companies striving for improved performances and increased competitiveness (Lampel & Giachetti, 2013, Reuter et al. 2016b). That is because GPNs provide companies with the potential to utilize cost benefits of manufacturing abroad as well as the ability penetrate new foreign markets (Moser et al., 2016; Peukert et al., 2020; Prinz & Bauernhansl, 2013; Reuter et al., 2016a; Schuh et al., 2012). In addition, dynamic competition and mergers lead to globally distributed manufacturing activities causing global production networks to expand and become increasingly complex, conclusively to tap full market potentials (Reuter et al. 2016b). Within this context, companies in global production networks face several challenges (Lanza & Moser, 2012; Olhager et al., 2015), which have been the focus of recent studies.
2.1.1 Challenges
Lanza and Moser (2012) focus their study on challenges related to uncertainty due to increasing market dynamics in the globalizing world, for instance in form of unpredictable market developments on an international scale and increasing customer requirements. The authors develop a method for companies to adapt their production networks to the changing global economy more effectively and efficiently. It does so by enabling companies to identify the need for change, as well as providing an assessment and a suggested strategy for future development. Olhager et al. (2015) also emphasize that these challenges are related to the current fast changing market dynamics, where it becomes increasingly difficult for companies to design,
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produce and distribute globally while also remaining efficient. The authors conducted a literature review on the design of GPNs. In it, they highlight the importance of network designs for companies with global footprints to remain competitive, for which location decisions are the main driver of change in these network designs. In light of this, Moser et al. (2016) present an approach to the optimal degree of changeability in GPNs. Through their study they are able to identify enablers for change on a network level in order to foster network flexibility by enabling migrations to new network structures, leading to improved competitiveness and operational efficiencies.
2.1.2 Efficiency and Costs
Operational efficiencies in terms of planning manufacturing volumes and allocating resources are necessary to leverage the full potential of GPNs (Schuh et al., 2012). According to the authors, this is true as the dimensions of making decisions are becoming increasingly complex with the continuous GPN growth. Hence, they take a practical approach and introduce an innovative software tool for improved processing of large quantities of data related to the decision-making process of production networks. Later, Schuh et al. (2014) present an approach which facilitates the decision-making process when designing GPNs. This approach is based on a process that evaluates the risks involved with each possible network configuration. This allows managers to make informed decisions about their network configurations based on risk to cost ratios. Moreover, Reuter et al. (2016b) introduce an approach that aims at finding the optimum condition for a network to operate considering cost and risk. This involves balancing the trade-off between the cost magnitude of manufacturing in another geographical area and the involved risk of manufacturing there. This is measured in terms of network robustness, meaning the impact of one site loss on the SC. In another study, Reuter et al. (2016a) develop a system that allows accurate benchmarking of manufacturing sites based on their individual performances. They do so by correlating performance in terms of Key Performance Indicators (KPIs), with site characteristics. This approach to performance measurement aims at companies to remain competitive within their respective GPNs.
2.1.3 Quality and Disruptions
Assuring product quality is another crucial component of successful production networks (Arndt & Lanza, 2016). The authors analyse GPNs from a perspective of product quality and the related unique challenges this brings to GPNs. They devise a method that allows global companies to plan quality control strategies for their production networks. By modelling
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individual network structures and assessing for quality control strategies across sites, the optimum quality control strategy can be identified. Treber et al. (2018) build on to this study, by introducing a new methodology to systematically investigate the causal relationship between disruptions through quality issues, quality assurance strategies and product quality. Furthermore, Treber and Lanza (2018) and Treber et al. (2019) in their respective papers derive methodologies to increase transparency in GPNs in order to mitigate negative influences of disruptions on the networks. By analysing the cause-and-effect relationships between disruptions, information exchange and operational performance, they identify information exchange between network partners as significant in relation to disruptions in production networks. Lastly, Peukert et al. (2020) focus on the negative effects of disruptions in GPNs. In their paper, the authors introduce a method for disruption management to identify appropriate responses to disruptions in GPNs for greater robustness in the network.
2.2
The Role of FDI in GPNs
According to the United Nation Conference on Trade and Development [UNCTAD] (2018), the outward flow of FDI to developing countries remained stable at $671 billion in the year prior to the COVID-19 crisis, despite a drop in inward FDI to the developed countries of 37% to $712 billion. In light of this, Schuh et al. (2017) furthermore explain that companies tend to shift to international manufacturing activities to pursue attractive markets. This is reinforced by Pavlínek (2018) using automotive FDI targeting less-developed countries as example. Here the automotive industry targets these countries to access large markets, or utilize the combined benefits of low-cost manufacturing, target market proximity, and access to regional trade agreements. This strongly indicates, that MNEs tend to use FDI to invest in GPN structures to utilize cost advantages of manufacturing in developing countries. In light of this, there are predominantly two motivations for MNEs to invest abroad, namely through horizontal FDI and vertical FDI (Westerberg & Andersson, 2008). The authors elaborate, that horizontal FDI refers to MNEs investing in full ownership of foreign production activities, in order to increase foreign market access to avoid tariffs and transportation costs when exporting finished goods. Vertical FDI on the other hand refers to MNEs globally dispersing their manufacturing activities, taking advantage of lower manufacturing costs of these countries within their SCs.
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2.3
Globalisation Strategies
A fundamental component of GPNs are corporate globalisation strategies. Within this context, Stentoft-Arlbjørn and Lüthje (2012) refer to globalisation strategies as outsourcing, insourcing, offshoring and reshoring activities within GPNs. Outsourcing and insourcing refers to the transfer of ownership and control of activities between the company and third parties (Stentoft & Rajkumar, 2020). Meanwhile, offshoring refers to the control and ownership of a company’s activities remaining within the company, combined with a geographical shift of these activities to another country (Johansson & Olhager, 2018). Consequently, back-shoring is the geographical relocation of manufacturing activities, owned and controlled by the company, back to the home country. Moreover, near-shoring is a process that involves relocating the aforementioned manufacturing activities near to the company’s home country (Martínez-Mora & Merino, 2020). Back-shoring and near-shoring are commonly placed under the umbrella term reshoring. Henceforth, our study refers to offshoring and reshoring as globalisation strategies.
2.3.1 Motivations to Offshore
Reshoring is becoming increasingly relevant, as there are numerous pieces of literature emerging in this field in the recent years. However, before being able to reshore, companies first need to offshore their manufacturing activities. This has varying motivations as depicted in Table 1.
Table 1; Motivations to Offshore
De Felice et al. (2021) introduce a mathematical decision model that facilitates the decision-making process for managers to determine the optimum manufacturing relocation as part of their competitive strategies. The model tests potential choices for offshoring, based on
Motivations to Offshore Authors
Conventional:
Cost advantages, labour, infrastructure, market access, and level of risk involved
De Felice et al. (2021); Wiesmann et al. (2017)
Unconventional:
Past personal and professional
experiences of decision makers, emotions of decision makers (patriotism, concern for the country's well-being)
Musteen (2016)
Environmental sustainability and customer perceived value
Lartey et al. (2020)
Augment ability to innovate, expand knowledge pool
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particular factors such as cost benefits, labour and infrastructure. According to the authors, basing decisions on these factors enables managers to remain competitive on the global market. This makes sense, as according to Reuter et al. (2016b) operational success in GPNs is measured by maximizing profits. Moreover, Musteen (2016) looks at drivers for offshoring beyond the conventional factors, such as labour costs, risks and market access, into the personal characteristics of decision makers. By analysing non-economic factors, namely past personal and professional experiences, emotions (e.g. concern for the country’s well-being and patriotism), and cognitive limitations, he finds that these factors also have a significant impact on decision makers to offshore.
Meanwhile, Wiesmann et al. (2017) conduct a systematic literature review in the field of reshoring as an emerging field, finding that due to the novelty of the field publications are only in a small number of journals where research strategies are either conceptual or empirically oriented. They importantly point out though that historically, companies executed hasty offshoring to utilize cost benefits of manufacturing. This led to current network structures being very complex which makes reshoring also a highly complex problem. Hence, the motivations to seek fast cost benefits through offshoring also resulted in a significant barrier to reshoring. Lartey et al. (2020) highlight the significance of environmental sustainability on offshoring strategies. They find that sustainability has an impact on offshoring strategies of MNEs, as it directly impacts the companies’ values and their impact on society. From another perspective, Musteen and Ahsan (2013) investigate the motivation of corporations to offshore and reconfigure their GPNs, based on seeking intellectual capital. This can have a positive influence on corporations’ capability to innovate, which however is also largely dependent on organizational flexibility, the organization’s ability to knowledge absorptive capacity and their ability to share information within the network.
2.3.2 Motivations to Reshore
After global enterprises have offshored one or more of their business activities, they might reconsider their options, leading to the concept of reshoring. The motivations to reshore are becoming increasingly prominent in recent literature. The following Table 2 summarizes these motivations.
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Table 2; Motivations to Reshore
Ancarani et al. (2015) investigate what determines the durations of companies’ offshoring strategies before they reshore, finding that country, industry and firm characteristics are relevant to this. Furthermore, they identify asset seeking motivations, such as quality and “made-in”, to also be relevant. Brennan et al. (2015) conduct an analysis on emerging trends, such as reshoring (e.g. cost-based relocations), new production systems (e.g. 3D printing), and lean programmes, finding that these changes trigger incremental instead of radical changes in global manufacturing. Next, Pal et al. (2018), through a literature review, identify success factors (e.g. flexibility for shorter lead times, high product quality, enhanced product and service customization) and challenges (e.g. costs, lack of resources and operational capabilities) to reshoring activities.
Fratocchi et al. (2016) in their study develop a framework that allows the classification of reasons for companies to reshore. Moreover, they are able to identify typologies of production relocation to be based on customer perceived value versus cost efficiency. Moreover, Srai and Ané (2016) look at the drivers of reshoring, finding that responsiveness and market proximity are dominant, but also network reconfiguration and restructuring require constant revision in today’s dynamic environment. Lund and Steen (2020) further identify harnessing regional
Motivations to Reshore Authors
Asset seeking motivations, such as quality and “made-in” tags, brand reputation
Ancarani et al. (2015); Moretto et al. (2020); Pal et al. (2018); Ancarani et al. (2021); Fratocchi et al. (2016)
Cost advantages, new production systems and lean programmes
Brennan et al. (2015); Moretto et al. (2020); Pal et al. (2018); Barbieri et al. (2019); Fratocchi et al. (2016);
Brandon-Jones et al. (2017) Market proximity, network responsiveness
due to market dynamics, operational flexibility, lead time reduction, enhanced product and service customization
Srai and Ané (2016); Moretto et al. (2020); Pal et al. (2018); Barbieri et al. (2019); Fratocchi et al. (2016)
Regional benefits: education and
manufacturing technologies, availability of qualified workers, proximity to home base, connected to innovation ecosystems
Lund and Steen (2020); Moretto et al. (2020); Pal et al. (2018);
Barbieri et al. (2019); Ancarani et al. (2021)
Corrections to previous offshoring decisions due to previously
underestimated risks and performance challenges
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benefits, such as education, and advanced manufacturing technologies as drivers for reshoring. Meanwhile, Moretto et al. (2020) find operational flexibility, availability of qualified workers, brand reputation, labour and logistics costs, lead time reduction, proximity to home base and tax incentives as further drivers to reshore.
Gray et al. (2017) study the decisions of U.S. based SMEs to reshore low-cost manufacturing activities from Asia. Their study shows that the decision of companies to reshore manufacturing activities from low-cost countries to high-cost countries is not only based on changes in relative costs. Hence, corrections to previous offshoring decisions were also motivated by previously underestimated risks and performance challenges involved with offshoring.
Barbieri et al. (2019) distinguish between reshoring back home and reshoring to a third country and are able to identify deciding factors for each. Accordingly, companies tend to reshore back home for market seeking purposes, this is especially true for the political and economic integration of the EU. Companies tend to reshore to third countries in order to pursue advantages in productivity. These companies likely pursued low-cost manufacturing strategies before such reshoring strategy. Furthermore, Ancarani et al. (2021) in a study of German and Chinese firms conclude that brand recognition based on the country of origin connected to innovation ecosystems plays a significant role in German companies returning manufacturing to Germany, but also Chinese companies to relocate their manufacturing activities to Germany. Lastly, Within the scope of the determinants for reshoring, Brandon-Jones et al. (2017) find that reshoring decisions also have a positive impact on shareholder value. The generated greater shareholder wealth may be explained by the economic advantages that is sometimes brought by manufacturing in high-cost countries when compared to the full costs involved of manufacturing in low-cost countries. Furthermore, from a public policy perspective reshoring may contribute to job creations, higher tax revenues, and economic development of the high-cost country.
2.4
Industry 4.0
‘Industry 4.0’ as a term is considered relatively recent and is still being defined in academia where academics are trying to gain an increased understanding of this new concept (Oztemel & Gursev, 2020). The same authors describe Industry 4.0 to represent the transformation going from a machine-based manufacturing to a digital-based manufacturing. Veile et al. (2020) further describe the importance of information and communication technology systems and
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internet-based connection in relation to Industry 4.0 as these technologies enable real-time data transfers and optimize decision making and business processes. However, since the concept is still considered rather immature, it is difficult to perform a full implementation (Oztemel & Gursev, 2020). This is further strengthened by Veile et al. (2020), who describe lack of experience related to Industry 4.0 technologies to be the main issue in regard to Industry 4.0 implementations.
Drivers and barriers to Industry 4.0 are further discussed under chapter 2.6 Industry 4.0 Drivers and Barriers. However, for increased understanding and elaboration, the different Industry 4.0 technologies are first discussed below together with specific drivers and barriers.
2.5
Industry 4.0 Technologies
As the definition of Industry 4.0 is still evolving, academia is inconsistent in what exactly Industry 4.0 comprise of. Frank et al. (2019) describe Internet of Things (IoT), cloud services, and big data analytics to be base technologies within Industry 4.0, as these are enablers for other smart Industry 4.0 technologies. Furthermore, a source that seems to be frequently cited is Rüßmann et al. (2015), who accents nine Industry 4.0 technologies; autonomous robots, simulation, horizontal and vertical system integration, IoT, cybersecurity, cloud technologies, additive manufacturing, augmented reality, and big data analytics. In addition to these nine technologies, Büchi et al. (2020) also add a section of ‘other technologies’, which compiles of industry specific technology. Raj et al. (2020) also mention Radio-Frequency Identification (RFID) and Artificial Intelligence (AI) as separate technologies, whereas others group these into other categories, e.g. Manavalan and Jayakrishna (2019) who mentions RFID to be a part of IoT. Oztemel and Gursev (2020) also choose to overlap simulation and augmented reality as these technologies both offer an enhanced version of reality with an added virtual layer of information.
Based on presented literature, following Industry 4.0 technologies will be further reviewed; IoT, cloud technologies, big data analytics, additive manufacturing, augmented reality and simulation, automation and robots, cybersecurity, and lastly horizontal and vertical integration. See Table 3 for a brief summary.
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Table 3; Industry 4.0 Technologies
2.5.1 Internet of Things
Büchi et al. (2020), describe IoT to be a set of connected devices and intelligent sensors that ease communication between different parties. There are several benefits of adapting IoT, specifically in a SC setting where real time connectedness has the ability to improve decision making and increase visibility, information sharing, and reduce costs (Ben-Daya et al., 2019; Manavalan and Jayakrishna, 2019). Similar benefits are described to exist in a manufacturing setting (Manavalan & Jayakrishna, 2019). The benefits of IoT are further enhanced in the
Industry 4.0
technologies Description Reference
Internet of Things
Internet of Things involves smart connected products which allows information sharing. Furthermore, IoT has gained increased attention in the manufacturing industry, and enables other new smart capabilities which can bring competitive advantages.
(Porter & Heppelmann, 2014)
Cloud technologies
Cloud computing efficiently support the storage and handling of large amounts of data, which further enables data-based systems and can lead to improved processes.
(Büchi et al., 2020)
Big data analytics
Big data analytics requires good structures and data-driven culture to successfully maintain large sets of data. Further, big data analytics make it possible for companies to capture opportunities that would not have been accessible without it.
(Grover et al., 2018)
Additive manufacturing
Additive manufacturing, also called 3D-printing, is manufacturing products through a controlled layering method which does not require final assembly.
(Berman, 2012)
Augmented reality and simulation
Simulation entails a virtually created environment of behaviours and situations etc. Augmented reality is instead described as an enhanced version of reality, and involves certain elements of simulation.
(Oztemel & Gursev, 2020)
Automation and robots
Artificial intelligence can enhance robots and their role in completing tasks in a cooperative way. Robots are furthermore, thanks to new technology, in some cases able to control their environment. Autonomation plays a part in smart factories and can with flexibility, autonomy, and data sharing enable new production technology.
(Oztemel & Gursev, 2020)
Cybersecurity
Cybersecurity involves the security of the
manufacturing plant and is ultimately described as a process of system design rather than particular added systems.
(Tuptuk & Hailes, 2018)
Horizontal and vertical integration
Horizontal or external integration entail integration between actors, whereas vertical or internal integration instead applies to cross-functional integration of information and communication technologies between departments and hierarchical levels.
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complexity of a global and international environment. In a manufacturing setting, IoT can reduce costs in relation to machine downtime, set-up, and errors due to the increased connectedness it brings, enabling what is called proactive maintenance (Büchi et al., 2020). Sisinni et al. (2018) divide the concept of IoT into two separate classifications, calling one category ‘consumer IoT’ and the other ‘Industrial IoT’ (IIoT). Consumer IoT is centred around human use (e.g. end-consumer) and can include technologies such as smart interconnected consumer electronics. Whereas IIoT is mainly machine oriented in comparison to the more human oriented consumer IoT. IIoT is seen as the basis of digital manufacturing where industrial assets are connected with business processes and information systems. Hence, it requires large amounts of collected data to be analysed to improve industrial operations. Sisinni et al. (2018) further describe IIoT to lead to highly autonomous production processes with little to no human interaction, where increased integration and connectivity offer improved efficiency between departments, plants, and machineries.
2.5.2 Cloud Technologies
Cloud technologies are simply described as an online data storage service that enables quick updates without required installations (Oztemel & Gursev, 2020). Veile et al. (2020) and Büchi et al. (2020) further describe cloud computing to allow storage of large amounts of data in a secure way with a high-level of efficiency and flexibility. Moreover, cloud systems are frequently discussed in relation with Big Data as it allows for larger data handling than a regular computer, consequently facilitating data analysis (Oztemel & Gursev, 2020). Manufacturing organisations who adopt cloud solutions therefore have less need to invest in advanced computers, and software updates (Alcácer & Cruz-Machado, 2019). However, cloud manufacturing requires investments in smart equipment to allow machines to communicate (Lu & Xu, 2019), where traditional networks usually are insufficient for real-time cloud manufacturing (Chen et al., 2018). Oztemel and Gursev (2020) further discuss the use of cloud-connected robots in a manufacturing setting, including elements of autonomous robots, to increase production speed and quality, for both small and large production companies.
2.5.3 Big Data Analytics
Big data exists in every digital process in our surroundings, always gathering and generating data from many different points in our society (e.g. social media) at a vast speed and great quantities (Oztemel & Gursev, 2020). Big data analytics includes technologies that handle and
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analyse these large amounts of data (Büchi et al., 2020; Oztemel & Gursev, 2020). Gathered data can originate from interconnected products, machines, people and processes, etc. (Büchi et al., 2020). The autors further mention some of the benefits in an organizational and SC setting of big data analytics to be fast communications, greater flexibility due to estimates, added efficiency, and limited costs. Frank et al. (2019) describe big data analytics as the enabling technology for advanced Industry 4.0 technologies. This is because it provides a high-level capacity of gathered, stored and analysed data, necessary to run other applications. According to Bag et al. (2020), big data analytics also offers strategic improvements that in turn can have a significant impact on organizations’ operational efficiency. Furthermore, this technological advancement contributes to more accurate decisions and can consequently achieve increased competitive advantage. Mentioned benefits further strengthen the SC through e.g. planning, inventory management, procurement, and logistics among others.
2.5.4 Additive Manufacturing
Additive manufacturing is described to be a manufacturing technique where no final product assembly is needed as the product is produced in layers, also known as 3D-printing (Büchi et al., 2020). Additive manufacturing consequently reduces machine down-time, set-up cost, and waste, hence leading to reduced production costs. However, this production technique requires cloud services (Dev et al., 2020). As additive manufacturing has the ability to produce a full module all in one place, thus also any place in the global SC, there is less need for particular suppliers and transportation (Ivanov et al., 2019). In addition, this technique enables a resilient SC as certain disruptions can be mitigated. Additive manufacturing machines are not shaped based on the product it produces, thus needing less set-up time and effort when switching between different products (Olsen & Tomlin, 2020). In addition to this, additive manufacturing machines have the possibility to produce different products indifferent of its shape, and consequently requires less space than multiple machines limited to produce one or fewer components, making these machines more flexible. Ivanov et al. (2019) explain how mentioned benefits can then help reduce inventory and unnecessary storage space, but also increase product customization.
2.5.5 Augmented Reality and Simulation
As according to Alcácer and Cruz-Machado (2019) augmented reality entails technical devices which allow access to a virtual environment that can act as a supporting tool for the task at hand. Simulation instead copies the real world virtually and allow for optimization in a virtual
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environment (Büchi et al., 2020), and enables tests and experiments to anticipate potential risks and consequences (Alcácer & Cruz-Machado, 2019). Simulation can further be used to increase understandings and gain insights into a complex system. A simulation software makes use of existing data and information to predict future outcomes and recommends certain solutions to avoid future problems, reduce costs, or suggest improvements (Fernández-Caramés & Fraga-Lamas, 2019).
Oztemel and Gursev (2020) choose to bundle together augmented reality and simulation as both technologies provide an enhanced version of reality, where the technology adds a layer of virtual information to the real world. This technology is currently getting more and more attention in the manufacturing setting as it can help avoid certain errors and improve processes. Moreover, this technology can also be used for other organizational functions in a company, some examples are marketing, and product design.
2.5.6 Automation and Robots
Robots and automated processes work with little error and less exhaustion than a traditional manufacturing due to reduced human involvement (Frank et al.,2019). The authors further distinguish between ‘collaborative robots’ and ‘automation & robots’, where automated robots are used to create automated processes. Whereas collaborative robots imply that robots work together with humans to improve humans’ productivity and flexibility. Another related concept of automation and robots are that of dark factories (Oztemel & Gursev, 2020). These factories have no direct human interaction and are fully automated. Many factories today have elements of automated processes but are still dependent on certain human support in some phases of production.
2.5.7 Cybersecurity
This technology concerns protecting flows of information and limits the risk of wrongfully spreading information (Büchi et al., 2020). Cybersecurity is deemed essential when protecting data of manufacturing in relation to big data platforms (Cui et al., 2020). Traditional control systems are more susceptible to attacks as they lack the necessary capabilities. Furthermore, integrated big data platforms require a closely integrated physical and virtual space, thus making cybersecurity important to avoid attacks on the physical system through the virtual space. In addition, Fernández-Caramés and Fraga-Lamas (2019) explain how Industry 4.0 is highly dependent on intra- and inter-connections, meaning that cybersecurity is necessary to
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protect these, especially in industrial critical systems. Cybersecurity in a manufacturing setting is however not widely researched and has a large potential for future research (Cui et al., 2020).
2.5.8 Horizontal and Vertical Integration
Horizontal and vertical integration are two different types of integrations within Industry 4.0 (Büchi et al., 2020). The former represents an external integration, where data and real time information is shared among actors along the SC, including suppliers and customers (Dalenogare et al., 2018). Whereas the latter can be explained as an internal integration, meaning that different areas within an organization are connected and share integrated information and communication technology systems. However, according to Veile et al. (2020) an internal integration requires large challenging organizational changes. An external integration instead faces the risk of data leakages and requires a certain level of trust and openness between involved actors. A successful horizontal integration however is further said to split risk, divide resources, and be fast to respond and adapt to market fluctuations (Dalenogare et al., 2018). Meanwhile a virtual integration further increases the visibility within the organization, allowing real time updated information independently of department or phase of production.
2.6
Industry 4.0 Drivers and Barriers
Besides implementation difficulties, Horváth and Szabó (2019) discuss other barriers and drivers of both SMEs’ and MNEs’ adaptation of Industry 4.0. Where MNEs are generally in an advantageous situation with lower barriers and higher driving forces than SMEs. The study in question researched following factors; human resources (HR), financial resources, market conditions and innovation, managerial and organisational factors, productivity/efficiency, and technology/process integration. Regarding HR, the study found labour shortages to encourage the implementation of advanced technology. However, HR could also be seen as a barrier as there is a shortage of skilled workers with the required talent for future operations. Moving on to financial resources, the reduced costs of HR, inventory, and operations were seen as a driver whereas the grand investments that are necessary for initial implementation is seen as a major barrier for companies. Furthermore, Horváth and Szabó (2019) describe advanced technologies to encourage innovation and hence also increase competitive advantages and may even be essential in an international setting with advanced economies. Additionally, the real-time information updates allow for transparency and grant management better control. However, not
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having access to competent management who understand and can control Industry 4.0 projects is seen as a major barrier, usually more evident in smaller companies. The study further describes the driver of productivity and efficiency based on less errors, shorter lead-times, and increased productivity, which in turn add flexibility and tie into quality and customer satisfaction. However, one major barrier discussed is the organisational resistance and outdated processes and organisational structures which can seriously hinder the introduction to advanced technology. This is especially true when employees are worried about losing their employment due to their lack of experience and skill required for the technological change. Based on chapters 2.4, 2.5, and 2.6, Table 4 shows summarized related drivers and barriers found in literature.
Table 4; Industry 4.0 Drivers and Barriers from Literature
Raj et al. (2020) further discuss a report by Schröder (2017) which gives insights into Industry 4.0 for SMEs in comparison to MNEs. It is then stated that MNEs have a greater adaptation and understanding of Industry 4.0 than SMEs, whereas manufacturing SMEs instead follow the standards of the MNE they supply for. One reason for this is that MNEs usually have more resources and more advanced systems to begin with, making it easier to invest in new advanced
Drivers of Industry 4.0 Authors Barriers of Industry 4.0 Authors2
Reduce human involvement
Horváth and Szabó (2019); Frank et al. (2019); Sisinni
et al. (2018)
Lack of talent, longer training Horváth and Szabó (2019)
Reduce costs (HR, inventory, operations, logistics)
Horváth and Szabó (2019); Ben-Daya et al. (2019); Manavalan and Jayakrishna (2019); Büchi et al. (2020); Alcácer and Cruz-Machado
(2019); Ivanov (2019)
Lack of financial resources, investments
Horváth and Szabó (2019); Lu and Xu (2019); Dev et
al. (2020)
Market competitiveness and innovation, follow trends
Horváth and Szabó (2019); Ivanov et al. (2019); Dalenogare et al. (2018)
Lack of management
skills/competencies, lack of planning Horváth and Szabó (2019)
Customer satisfaction (quality and related risks)
Oztemel and Gursev (2020); Alcácer and Cruz-Machado (2019); Horváth and Szabó
(2019)
Organizational factors (resistance to change, different goals, unmatching org. Structure)
Horváth and Szabó (2019); Veile et al. (2020)
Increased productivity/efficiency (less lead times, downtime, and errors)
Horváth and Szabó (2019); Caylar et al. (2016); Oztemel and Gursev (2020);
Bag et al. (2020); Alcácer and Cruz-Machado (2019)
Technological and process cooperation (lack of standards, communication, cybersecurity)
Horváth and Szabó (2019); Veile et al. (2020)
Increased visibility and information sharing (integration), control (decision-making)
Ben-Daya et al. (2019); Manavalan and Jaya
krishna (2019); Bag et al. (2020); Dalenogare et al. (2018); Sisinni et al. (2018); Büchi et al. (2020); Horváth
and Szabó (2019)
Lack of knowledge of Inudstry 4.0 Oztemel and Gursev, (2020); Veile et al. (2020)
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technology. Due to the nature of large companies’ process management, large companies will generally achieve higher efficiency than smaller companies (Schröder, 2017). In addition to this, Raj et al. (2020) review a McKinsey report by Caylar et al. (2016) stating that an automated Industry 4.0 production can in certain industries increase productivity by 45-55% in comparison with traditional manufacturing, further enhancing the benefits Industry 4.0. In addition, MNEs are said to be more dependent on the financial profitability due to their large quantities in comparison to smaller companies where Industry 4.0 may initially be introduced for other reasons (Horváth & Szabó, 2019). Similarly for productivity, efficiency and control where there is a constant strive for optimization in MNEs. Moving on, due to the global environment MNEs operate in, HR barriers are somewhat mitigated as they can utilize global talent where SMEs are more dependent on local talent.
2.7
Framework
Nascent literature in the field of GPNs and globalisation strategies of global corporations predominantly focusses on product quality, market dynamics, organizational costs and profitability, and access to resources. Accordingly, these can be described as the main factors to consider when organizations go global via offshoring, or when organizations relocate their already global manufacturing activities by reshoring. The current literature examined on Industry 4.0 within this theoretical framework indicates that Industry 4.0 as an upcoming innovative trend of smart manufacturing, has the potential of facilitating these organizational globalisation strategies. Based on reviewed literature, the framework in Figure 2 has been developed. This framework shows the current theoretical understanding of Industry 4.0 and its potential relation to globalisation strategies.
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Figure 2; Framework
Stentoft and Rajkumar (2020) see this as well as they also study the influence of Industry 4.0 related drivers and barriers on globalisation strategies. However, the globalisation strategies they refer to are with focus on offshoring and reshoring decisions of SMEs. The authors conclude that the drivers and barriers, that were identified for SMEs, do have a significant impact on their offshoring and reshoring decisions. However, they point out that MNEs were not considered within their theoretical framework and identify this to require further exploration.
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3. Methodology
___________________________________________________________________________
The methodology chapter introduces the reader to the philosophical approach and research methods. The research design is further justified and explained below, together with data collection, quality assurance, and research ethics.
___________________________________________________________________________
3.1
Research Philosophy
Understanding and the awareness of philosophical assumptions is an important first step in conducting quality research (Easterby-Smith et al., 2018). As a researcher it is therefore important to first understand in which ontology and epistemology one is conducting science. The philosophical debate begins with how we define the nature of reality, namely ontology. The ontological assumptions can often be based on the field of research, where differences are drawn between the natural and social sciences (Bryman & Bell, 2011). One ontological perspective is relativism, which focusses on observations made about phenomena from different perspectives (Easterby-Smith et al., 2018). This assumes that for example theories are acknowledged by the consensus of the scientific community, which makes relativism a suitable ontology for the purpose of this study. This is as phenomena created based on the contextual perspective of various individuals. Analysing the strategic decisions of managers within MNEs regarding their globalisation strategies in the context of Industry 4.0 drivers and barriers puts this study within the social sciences. Furthermore, it is necessary to analyse the relation between Industry 4.0 drivers and barriers and globalisation strategies of MNEs in practice from perspectives of different decision makers to successfully inquire into this topic and state an acceptable answer for the global manufacturing industry.
Understanding the ontological assumptions of this study furthermore requires an understanding of the related epistemology. According to Easterby-Smith et al., (2018), epistemology entails how we inquire into the worlds surrounding us and educates on what knowledge is. There are two practices within epistemology, namely positivism and constructionism. Positivism emphasizes objectivity, as the properties of the external world should be investigated by objective measurements. Constructionism on the other hand assumes that reality is formed by the varying perspectives of people. Hence, studying the experiences of people and how they make sense of the world puts constructionism on the subjective side of the spectrum. Based on this it can be deduced that the assumptions of constructionism are most suitable for this study. The aim of this study is to study the individual experiences of decision makers in GPNs and
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investigate how these managers act and make decisions based on how they see the relation between Industry 4.0 drivers and barriers and globalisation strategies of MNEs in practice.
3.2
Research Approach
The research approach is closely linked to the research paradigms and the proximity of the researcher to the subject of study (Easterby-Smith et al., 2018). The authors explain that there is positive value in getting close to the subject of study, such as complex organizations as social systems and term this as ’engaged’ research. The authors proceed by illustrating the combined dimensions of engaged-detached and the epistemologies of positivism-constructionism on a matrix with two axes. The created quadrants enable the positioning of various research approaches and philosophies in relation to their respective epistemological positions and their engagement or detachment to the subjects of a study, see Figure 3 below.
As previously determined, the study is positioned within the constructionist paradigm. When combining this epistemology with a higher engagement one research approach stands out, namely pragmatism. Easterby-Smith et al., (2018) highlight the key point of pragmatism to be the experiences of individuals which produce meaning or structures. Therefore, considering this study, this approach is suitable to investigate the drivers and barriers of Industry 4.0 and their
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influence on the motivation of MNEs to offshore or reshore their production activities. It is crucial to collaborate closely with practitioners in the field, to gain deeper insights into the relation between Industry 4.0 drivers and barriers and globalisation strategies of MNEs in practice. Moreover, these direct experiences are hence utilized to produce meaningful data to advance the theoretical knowledge within the field.
In addition to the research philosophy, the position of the researcher and the level of engagement with the subjects of study, there are several methodological approaches to conducting research (Graneheim et al., 2017). Relevant for this study is the inductive approach as it involves the categorization of data based on similarities and differences. Through the search of patterns to describe the data, the researcher develops theories, which is also referred to as moving from a specific to an abstract level. This is relevant to this study, as it utilizes the collected data in combination to develop the theoretical understanding of drivers and barriers of Industry 4.0 and their relation to the globalisation strategies of MNEs and their GPNs.
3.3
Research Design
In this section, research tools and choices are logically described and justified to properly answer this study’s research questions. This includes the procedures of gathering, comparing, and analysing data. See Figure 4 for overall structure and research approach, and appendix A for the thesis timeline. The theoretical framework and the multiple case study together help answering the first research question. The first research question is in turn connected to the second research question. Once both research questions have been addressed, these are applied accordingly to address the research purpose.
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Skärvad and Lundahl (2016) describe qualitative research to bring insight and in-depth understanding to the researched phenomenon. They further describe qualitative research to strive for increased understanding of the process and motives behind an individual’s decision-making. As this study gives insights into the decision-making process regarding offshoring and reshoring strategies, a qualitative approach will be conducted, which further aligns with the ontological and epistemological views of the study.
Qualitative research, specifically inductive research, is commonly of exploratory alignment (Guest et al., 2012). Exploratory research has no predetermined codes but rather searches for codes or categories in gathered data. However, this does not mean that pre-existing data is absent. Even in explorative research, guiding material is necessary to support the research process. Henceforth, this study has an exploratory approach where existing literature is used as guiding material. This further aligns with our inductive research approach.
3.3.1 Thematic Analysis
Creswell (2013) describes all qualitative analysis methods to have similar core structures. These consist of coding the gathered data, categorize the codes, and finally compare and discuss data through either text or visuals. Firstly, the author encourages organisation of the data set to easier make sense of large amounts of data. Once there is a fundamental understanding of the data set, the coding process can begin. Codes are developed based on the data set and is later aggregated into relevant themes or categories. It can be challenging to condense large sets of data to codes and categories, but it is helpful to facilitate understanding and interpretation of these large quantities of data that are common in qualitative research.
Thematic analysis is a data analysis method that specifically searches for ideas or codes in raw data (Guest et al., 2012). Thematic analysis is described by the authors to be commonly used in qualitative research and is especially useful when analysing complex meanings in data sets. Empirical data can vary in complexity but frequently consists of transcribed in-depth interviews that are 1-2 hours long (Guest et al., 2012), and is frequently applied in multiple case study research (Creswell, 2013). Applied thematic analysis is further described by Guest et al. (2012) to be similar to both grounded theory and phenomenology but is broader in its use and offers greater flexibility. In summary, applied thematic analysis can facilitate the reduction of an extensive data set to codes in an aggregated manner to identify themes in data in a transparent fashion.
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For this study, a thematic analysis method is applied to identify themes across the data set. We start by thoroughly go through all the data separately to better understand the data and to be able to organise it before the coding process begin. From raw data we create first level codes, these are then bundled together into second level codes, which lastly creates relevant themes. This process was done individually, whereafter we came together and compared our codes, to come to a more neutral approach. Merging our individual codes then in some cases required further iterations of recoding, which mitigates biases of each individual.
3.3.2 Multiple Case Study
Yin (2018) discusses several different research methods, where case study as an empirical method suits our study the best. Case studies allow research to “investigate a contemporary
phenomenon in depth and within its natural context, especially when the boundaries between phenomenon and context may not be clearly evident” (Yin, 2018, p. 15). As according to
Skärvad and Lundahl (2016), case studies are a commonly used instrument in qualitative research and aim to provide in-depth research on one or several cases. Considering the complexity of this study’s context in relation with the research questions and purpose, a qualitative in-depth case study can create a deeper understanding of the situation and enables clearer insights into the motives behind business decisions.
Yin (2018) recommends a multiple case-study, as these can be seen as more substantial than single case-studies. In addition, multiple case-studies, can essentially increase generalisability of the study (Yin, 2018), and allow cross-case comparisons (Bryman & Bell, 2011). As this study aims to create a broad understanding of the research topic, multiple cases are preferable to increase the number of perspectives and give a fuller picture of the motives. For the same reason, case companies within manufacturing across different sectors are selected to preferably give a broader result and understanding.
3.3.3 Case Selection
This study requires interviewees that fulfil certain criteria to be able to contribute to the study. Therefore, purposive sampling is a suitable option (Easterby-Smith et al., 2018). As according to Easterby-Smith et al. (2018), purposive sampling is a non-probability sampling strategy, where specific criteria are formulated and are necessary for inclusion in the study’s sampling. Consequently, potential candidates need to be screened according to these pre-determined criteria to ensure that all participants qualify before partaking.
