Blockchain technology adoption in agri-food supply chains: why or why not? : Exploring Swedish organizations’ reasoning and approach to adoption

Blockchain technology

adoption in agri-food

supply chains: why or

why not?

BACHELOR THESIS WITHIN: Business Administration NUMBER OF CREDITS: 15 ECTS

PROGRAMME OF STUDY: Marketing Management AUTHOR: Tilda Lindén & Johanna Persson

JÖNKÖPING May 2021

Exploring Swedish organizations’ reasoning and

approach to adoption

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Acknowledgements

We would like to thank everyone who has contributed to this thesis. First, we would like to express our gratefulness to our tutor, Quang Evansluong, for supporting and guiding us through the whole process by providing rich knowledge and feedback.

Secondly, we want to show appreciation to all participating organizations and each interviewee for their commitment and sharing of valuable knowledge and insight for this thesis.

In addition, we would like to thank Anders Melander for providing the guidelines for the thesis process and show special appreciation to Judith Wolst for planting the idea of blockchain technology.

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Bachelor Thesis in Business Administration

Title: Blockchain technology adoption in agri-food supply chains: why or why not?

Authors: Tilda Lindén & Johanna Persson

Tutor: Quang Evansluong

Date: 2021-05-23

Key terms: Industry 4.0; Blockchain technology; Agri-food industry; Supply chain management; Technology adoption

Abstract

Background:

Industry 4.0 technologies are expected to play an important role in the near future. Among these, blockchain technology is in the spotlight and recognized to be revolutionary within the agri-food industry and its supply chains. However, the technology and its adoption is in an early phase and involves several challenges for agri-food organizations. Given its nascent nature, academic research is scarce and a need for research of blockchain technology adoption in different contexts has been identified.

Purpose:

The purpose of this thesis is therefore to analyze the reasoning behind Swedish agri-food organizations’ decisions to adopt or reject blockchain technology as well as their approach to its adoption in their supply chains.

Method:

A qualitative research design with an inductive approach was applied, where the primary data was gathered through 9 semi-structured interviews with agri-food organizations and experienced individuals within the field.

Conclusion:

The findings show that trustworthiness is the main goal and driver of blockchain technology adoption and identified several secondary reasons for adoption. The research also specifies challenges which act as reasons for rejection as well as two-edged critical factors affecting adoption decisions. Further, Swedish agri-food organizations were recognized to be in an immature adoption phase and hence two main approaches to blockchain adoption, proactive and pending, were determined. Based on these findings, the BAP framework visualizing the blockchain adoption process was developed.

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Table of Contents

... 1

1.

Introduction ... 1

2.

Frame of Reference ... 7

2.1 Method for Frame of Reference ... 7

2.2 Technology Adoption of I4.0 in SCM ... 8

2.2.1 Industry 4.0 ... 8

2.2.2 Adopting I4.0 Technologies in SCM ... 9

2.2.3 I4.0 Technology Adoption in the Agri-food Industry ... 11

2.3 BCT in SCM ... 12

2.4 BCT Adoption in Agri-food Supply Chains ... 14

2.4.1 Adoption Benefits & Drivers ... 17

2.4.2 Adoption Challenges & Barriers ... 20

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Methodology & Method ... 22

3.1 Methodology ... 22 3.1.1 Research Paradigm ... 22 3.1.2 Research Approach ... 23 3.1.3 Research Design ... 23 3.2 Method ... 24 3.2.1 Primary Data ... 24 3.2.2 Sampling Approach ... 24 3.2.3 Semi-structured Interviews ... 27 3.2.4 Interview Questions ... 28 3.2.5 Data Analysis ... 28 3.3 Ethics ... 29

3.3.1 Anonymity and Confidentiality ... 30

3.3.2 Credibility ... 30

3.3.3 Transferability ... 31

3.3.4 Dependability ... 31

3.3.5 Confirmability ... 31

4.

Empirical Findings & Analysis ... 32

4.1 Conducting the Result & Analysis ... 32

4.2 Trustworthiness as a Driver of BCT Adoption ... 33

4.2.1 Organizations Aim to Create Added Value for Stakeholders ... 34

4.2.2 Organizations can gain Benefits of BCT ... 36

4.3 Organizational Conditions affect BCT Adoption Decisions ... 39

4.3.1 Internal Necessities of BCT Adoption ... 40

4.3.2 Supply Chain Conditions ... 42

4.3.3 Organizations’ Concerns about BCT Adoption ... 44

4.4 Immature Phase of BCT Adoption ... 45

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4.4.2 Approaches to BCT Adoption ... 47

4.4.3 External Necessities to BCT Adoption ... 48

5.

Conclusion ... 50

6.

Discussion ... 52

References ... 55

Appendices ... 64

Appendix 1: Interview questions for agri-food organizations ... 64

Appendix 2: Interview questions for experienced individuals ... 66

Appendix 3: GDPR consent form ... 68

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1. Introduction

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––

This chapter aims to give a background to the topic of the Industry 4.0 technology, blockchain, and its relevance for Swedish agri-food supply chains. To do so, a general picture of blockchain technology as well as technology adoption in the Swedish agri-food industry is provided. Further, the problem, purpose and research questions will be outlined to provide a deeper understanding of the importance and aim of this thesis. These sections are then followed by the thesis delimitation as well as definitions of relevant concepts.

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Background

Industry 4.0, hereinafter called I4.0, involves a constant emerging field of technological trends that firms adopt on different levels in different capacities to improve current processes and achieve a competitive position in the market (Haddud & Khare, 2020). Although many of the I4.0 technologies are not yet being fully utilized, they are anticipated to play an important role in the near future (Xu et al., 2018). One of those is blockchain technology, hereinafter called BCT. Originally, BCT is a method that allows trading and traceability of financial assets (Yermack, 2017) which gained popularity for being a platform that managed the digital cryptocurrency Bitcoin (Nakamoto, 2008). However, the concept of BCT has developed and today it also creates value in areas such as manufacturing (Xu et al., 2018), international insurance, marketing and supply chain management (Hooper & Holtbrügge, 2020). BCT is considered both revolutionary (Bumblauskas et al., 2020) and promising (Hughes et al., 2019) for the agri-food industry as it makes it possible to solve its current supply chain challenges (Köhler & Pizzol, 2020). Studying BCT in supply chain management, hereinafter called SCM, has gained increased interest in academic research. However, adoption of BCT in SCM is limited (Gurtu & Johny, 2019; Shoaib et al., 2020), and increased research of BCT adoption related to specific countries (Kamble et al., 2020; Queiroz et al., 2019), industries and supply chains is needed (Queiroz et al., 2019). As opposed to in other fields, academic research in the field of digitalization and greening of supply chains is leading industry

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practice (Sarkis et al., 2020). Thus, increased academic research of BCT adoption in the Swedish agri-food supply chains is needed to provide practical guidance.

The agri-food industry is considered to have demanding consumers, which require a more transparent and safe food supply chain (Cointelegraph Consulting, 2020; Tiscini, 2020). This increasing consumer demand has been found to impact the probability of BCT adoption in the agri-food industry (Bumblauskas et al., 2020). Not only the consumers are becoming increasingly demanding as global challenges increase stakeholders’ demand for a more sustainable future (Saberi et al., 2019). United Nation’s sustainability goals, especially goal 2 Zero hunger and goal 12 Responsible production and consumption, are driving the need for increased traceability and transparency in global food supply chains (Kairos Future, 2017; Sharma et al., 2020). Academic research also points out that the agri-food industry plays a significant role in achieving UN's sustainability goals (Sharma et al., 2020).

BCT has been recognized as a solution of many agri-food supply chain challenges, for example transparency and traceability (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Zhao et al., 2019), thus it can remove the gap of trust between producer and consumer (Cointelegraph Consulting, 2020) as it, for example, can let consumers know the journey of the food they eat (Bumblauskas et al., 2020; Tiscini et al., 2020). Even though BCT is a fairly young technology in an immature implementation stage (Chen, et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Wang et al., 2019), the experiments done in the agri-food industry tend to gain attention and increased interest by other actors (Da Costa Guimarães et al., 2020). When researching technology adoption, Scandinavian countries have been found to take on a high level of adoption of I4.0 Infrastructure (Castelo-Branco et al., 2019). However, research of BCT adoption related to the Swedish agri-food industry is scarce. The vision of the Swedish food strategy 2030 is that “The Swedish food chain year 2030 is globally competitive, innovative, sustainable and attractive to operate within.” (Johansson, 2021, p. 4, my translation). However, this is not yet the case as the Swedish food chain primarily includes small companies in which the lack of competence and investment capacity hinder their ability to invest in research and development (Johansson, 2021). The annual report from Jordbruksverket written by Burman et al. (2020) shows that the low level of education in

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the food chain hinder innovation and ability to assimilate new technology and research findings. Also, the results of the report display that little investment is allocated to research and innovation in the food chains compared to other sectors. Food production is becoming more high-tech and knowledge intensive, thus knowledge lead is becoming the main means of competition. This is an area where the Swedish agri-food companies are in risk of lagging behind the global development in case no change is initiated. However, as Sweden currently struggle to develop and encourage innovation in the agri-food industry the industry has great potential in developing innovation opportunities (Johansson, 2021).

Problem

Today, SCM is facing many challenges. Challenges which the implementation of various I4.0 technologies can help solve as previous research shows a strong relation between I4.0 and SCM (Haddud & Khare, 2020). Research in the field of I4.0 has concluded that technologies are many (Chiarello et al., 2018), concepts lack common definitions (Hofmann & Rüsch, 2017; Queiroz et al., 2019) and implementation is considered complex and difficult (Frank et al., 2019). To deal with technology adoption barriers, researchers suggest that organizations need to prepare and transform their businesses both at an organizational and managerial level (Shao et al., 2021; Agostini & Filippini, 2019). However, there is a lack of research regarding the organizational and managerial aspects of I4.0 (Horváth & Szabó, 2019) as well as little research conducted within the application perspective of I4.0 technologies (Liao et al., 2017). Thus, the ability to transform research proposals into actual implementation is constrained. This is important to address as further research is needed to guide implementation of I4.0 technologies and fulfill the need of identifying solutions of resources needed for implementation in specific industries and supply chains (Zekhnini et al., 2020). Thus, I4.0 can help solve supply chain challenges, but implementation is challenging, and more research is needed. BCT is one of the fairly new I4.0 technologies (Köhler & Pizzol, 2020; Tiscini et al., 2020; Chen, et al., 2020) which can benefit agri-food supply chains (Chen, et al., 2020; Dutta et al., 2020). Research in the field of BCT adoption in agri-food supply chains has identified several benefits and drivers. The most frequently mentioned is increased

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traceability and transparency (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Zhao et al., 2019), enhanced food safety (Tiscini et al., 2020; Zhao et al., 2019), food quality (Chen et al., 2020; Tiscini et al., 2020; Zhao et al., 2019), trust (Chen et al., 2020; Feng et al., 2020; Zhao et al., 2019), efficiency (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020) as well as benefits caused by its dis-intermediation (Kamble et al., 2020; Tiscini et al., 2020) and immutability (Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Zhao et al., 2019). Most researchers also claim that it can enhance organizations in their accomplishment of a more sustainable supply chain (Dutta et al., 2020; Feng et al., 2020; Hughes et al., 2019; Kamble et al., 2020; Kouhizadeh et al., 2021; Köhler & Pizzol, 2020; Tiscini et al., 2020). Thus, the adoption of BCT in agri-food organizations can be seen as a business opportunity. However, academic literature in the field of BCT adoption in agri-food supply chains is scarce and no academic research of BCT adoption specific to the Swedish agri-food industry has been found. This issue needs to be addressed as scholars highlight a need for further research of BCT implementation and adoption (Dutta et al., 2020; Saberi et al., 2019) as well as such research related to specific countries, industries and businesses (Queiroz et al., 2019). Furthermore, Kamble et al. (2020) stress the need for identifying adoption enablers related to specific countries. BCT adoption in agri-food supply chains involves many benefits and drivers. However, increased research of BCT adoption in specific contexts is needed.

Previous research tends to show that BCT is in an immature implementation stage within the agri-food industry (Köhler & Pizzol, 2020; Tiscini et al., 2020; Chen, et al., 2020). The majority of current research in the field of BCT adoption is focusing on the benefits of adoption. However, research also highlight barriers of adoption (Shoaib et al., 2020). Within agri-food organizations, barriers related to a lack of knowledge (Tiscini et al., 2020; Zhao et al., 2019), technical challenges (Feng et al., 2020; Kamble et al., 2020), high financial investment (Chen et al., 2020; Tiscini et al., 2020; Zhao et al., 2019), legal and regulatory issues (Feng et al., 2020; Chen et al., 2020; Kamble et al., 2020; Zhao et al., 2019) as well as supplier engagement (Chen et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020) has been identified. According to Gurtu and Johny (2019) and Shoaib et al. (2020), not enough research regarding BCT related to SCM is provided. Also, current research of adoption lacks the investigation of Swedish perceptions and

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approaches. Further, a lack of clear theoretical explanations of adoption attempts has been identified (Nandi et al., 2020). Thus, it is important to contribute with research in this field to encourage the development of BCT-enabled supply chain systems, hence strengthening the reasoning behind the purpose provided in the following section.

Purpose

The purpose of this thesis is to analyze the reasoning behind Swedish agri-food organizations’ decisions to adopt or reject BCT as well as their approach to adopting BCT in their supply chains. To do so, it will investigate the underlying benefits, drivers, challenges and barriers which affect organizations’ decision-making and approach towards BCT adoption. Further, by specifically focusing on BCT in the context of the Swedish agri-food industry, the thesis is expected to make a theoretical contribution by advancing current knowledge and insights of BCT adoption as well as providing new mapping of drivers, benefits, challenges and barriers of adoption in Swedish agri-food supply chains. Thus, valuable insights and guidelines for Swedish agri-food organizations will be provided.

Research Questions

To achieve the purpose of this thesis, the following research questions are proposed:

RQ1: What are the main reasons behind organizations’ decisions to adopt or reject BCT

in the Swedish agri-food industry?

RQ2: How does agri-food organizations approach the adoption of BCT in SCM?

Delimitations

This thesis will be delimitated to investigating the adoption of BCT, which is one of many technologies that I4.0 involves (Chiarello et al., 2018). However, focus is not on a specific BCT architecture but instead the organizational approach of the technology adoption in relation to SCM. The implementation of I4.0 technologies in SCM can often be related to sustainability (Bai et al., 2020). The same is evident when it comes to BCT (Saberi et al.,

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2019). However, this thesis does not focus on studying the sustainability perspective but rather the adoption perspective, where sustainability can be seen as an outcome and driver of BCT adoption in agri-food supply chains (Feng et al., 2020; Kamble et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020). Another thing to keep in mind is that the frame of reference does not review Swedish literature due to the limited, almost nonexistent, academic research within a Swedish context.

Definitions

This thesis will frequently refer to the concepts: Industry 4.0, Blockchain technology and Agri-food supply chain, hence definitions of those are provided (see table 1). Neither of the concepts are referred to using one single definition. Therefore, the most common explanations used in previous academic research is chosen.

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2. Frame of Reference

–––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––––– This chapter aims to provide a frame of reference for this thesis by positioning it against relevant research in the field. To answer the research questions of this thesis, the frame of reference covers the main areas of the research: Technology adoption, SCM, BCT and the Agri-food industry. These main areas will be covered by three sections: (2.1) Technology adoption of I4.0 in SCM, (2.2) BCT adoption in SCM, (2.3) BCT adoption in agri-food supply chains. Presenting the topics in this logic is necessary in order to first provide an understanding of the context of I4.0 technology adoption and how it is related to SCM and the agri-food industry. Secondly, technology adoption in SCM is narrowed down to solely focusing on the adoption of the I4.0 technology, blockchain. Lastly, the main section intertwines the main areas of the research by addressing current literature of BCT adoption in agri-food supply chains. By providing this logic, the frame of reference covers the literature needed to enable an analysis of primary findings and help answer the proposed research questions. Before the main parts, the method of the frame

of reference will be presented.

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2.1 Method for Frame of Reference

When conducting the frame of reference of this thesis a collection of databases was used to gather relevant literature, predominantly Business Source Premier, Scopus and Primo. Examples of keywords used to find relevant academic literature were industry 4.0,

blockchain technology, agri-food and supply chain management. The articles found

through advanced searches contributed to a deeper understanding of I4.0 technologies such as BCT in the context of SCM in the agri-food industry. Combining the different keywords, generated a limited number of studies and therefore the authors searched for relevant literature within already gathered articles and through ABS ranked journals. The literature was critically reviewed by the authors to ensure relevant literature for the topic studied. Further, articles were selected with respect to the qualitative judgement criterion, supported by Saunders and Lewis (2012). To ensure quality of the literature, only peer reviewed articles were used, and all articles belonged to journals ranked in the ABS list, for example International Journal of Production Research. Other details that were

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considered when choosing articles were number of citations and year of publication. To further increase the quality of the selected studies, the authors chose studies published after 2015. The majority of the published studies within the main areas of research were published during the last three years, hence explain the newness and might be a reason for the limited research.

2.2 Technology Adoption of I4.0 in SCM

This section presents an explanation of I4.0, followed by an analysis of how its disruptive technologies are adopted in relation to SCM. Thereafter, an overview of how I4.0 impacts the agri-food industry will be presented. Providing this theoretical context is necessary to further understand the complexity of BCT adoption.

2.2.1 Industry 4.0

Industry 4.0 (I4.0) cannot be considered a future trend anymore (Xu et al., 2018). It is a present revolution which has gained increased interest in academic research (Liao et al., 2017; Osterrieder et al., 2020) and general publications (Osterrieder et al., 2020). Much academic research of I4.0 is related to manufacturing and sustainability.

I4.0 is shifting our manufacturing (Frank et al., 2019; Hofmann & Rüsch, 2017; Xu et al., 2018). It can enhance both efficiency, competency (Xu et al., 2018) and enable manufacturing to become increasingly decentralized and self-regulated of value creation (Hofmann & Rüsch, 2017), a phenomenon which is called smart manufacturing (Frank et al., 2019). Further, there is a positive relation between sustainable production and I4.0 adoption (Bag et al., 2021) as it can contribute to social and environmental sustainability (Bai et al., 2020). Therefore, the interest of implementing I4.0 technologies for sustainability purposes has increased and is expected to continue to do so because of the global sustainability impact of our supply chains. Although research in the field of I4.0 is increasing, technologies are many (Chiarello et al., 2018) and concepts are considered fuzzy (Hofmann & Rüsch, 2017).

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I4.0 includes more than 30 various fields of technologies (Chiarello et al., 2018) and hence it is often recognized as being rather complex. Also, the implementation of I4.0 technologies is challenging (Frank et al., 2019) and research is lacking a common definition of I4.0 itself (Hofmann & Rüsch, 2017). Companies find it difficult to form a concrete strategy because of the confusion of different terminologies, ideas and concepts within the smart factory concept (Osterrieder et al., 2020). The huge number of different technological fields makes stakeholders feel uncomfortable leading to communication challenges (Chiarello et al., 2018) and lack of knowledge, which is considered a barrier (Winkelhaus & Grosse, 2020). Also, the lack of standardization of these technologies combined with the uncertainty regarding the economic costs and values are considered barriers of implementation (Winkelhaus & Grosse, 2020). The barriers and challenges related to the implementation of I4.0 technologies slows down the pace of the revolution and there is little literature within the application perspective of I4.0 leading to difficulties of transforming research proposals into actual implementation (Liao et al., 2017). Although I4.0 has gained increased interest in terms of manufacturing and sustainability, it is about digitization, a process which impacts the whole supply chain (Matt et al., 2015). To understand this better, the following section provides a review of research regarding I4.0 adoption in SCM.

2.2.2 Adopting I4.0 Technologies in SCM

Previous research tends to show a significant relation between I4.0 technology adoption and SCM (Haddud & Khare, 2020; Shao et al., 2021). A digitalized supply chain can increase overall performance and benefit businesses (Haddud & Khare, 2020; Shao et al., 2021) for example, through better visualization and traceability, more proper processes and better ability to respond to changes as well as more engaged suppliers and customers (Haddud & Khare, 2020). When implementing these technologies, it is important to focus on the supply chain level to achieve benefits all along the supply chain from consumer to supplier (Shao et al., 2021). Digitalization and the development of I4.0 is reshaping the structure of supply chains, which requires businesses to update and innovate their supply chain operations to remain relevant in the market (Garay-Rondero et al., 2019; Liotine, 2019). Also, businesses need to rework their strategies to increase transparency (Seyedghorban et al., 2020). However, adoption of technical innovations involves large

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investments and risks (Liotine, 2019; Zekhnini et al., 2020). Haddud and Khare (2020) further argue that it is necessary to assess conditions of the business environment and networks and explore which part of the supply chain that benefits through digitalization. Digital supply chains and implementation of I4.0 at the supply chain level gain more attention but there is still a lack of research (Seyedghorban et al., 2020; Shao et al., 2021). For example, Shao et al. (2021), suggests future studies to further research I4.0 technologies and their applications processes along supply chains and the dynamic between supply chain partners relationships. Also, it might be difficult for managers and potential users to determine which technologies that are the most suitable to a particular supply chain and to see the value and impact of implementing it (Zekhnini et al., 2020). Adoption of I4.0 has a specific relation to SCM, however this adoption requires organizational change.

Digital technologies require a challenging and complex transformation of operational structure and management approaches (Matt et al., 2015) thus might involve changes at organizational and managerial level (Agostini & Filippini, 2019). A change often occurs in conjunction with organizational resistance which can be considered as a dominant boundary to technology adoption (Horváth & Szabó, 2019). To deal with the challenges, businesses need to create a general understanding of change and develop practices that can support employee’s tolerance and competence of changing environments. Agostini and Filippini (2019) mean that implementation of I4.0 requires engagement and involvement of all business levels, both the organizational and managerial level need to be ready before implementing and managing the technology. Further, expectations of management are one main driver of the I4.0 adoption since digitalization supports decision-making and the assessment of the business and employees (Horváth & Szabó, 2019). Moreover, adoption is challenging due to a lack of strategic and detailed guidelines of how to implement and evaluate the implementation (Matt et al., 2015; Zekhnini et al., 2020). There is a future need for a roadmap that identifies solutions of issues and resources needed for implementing I4.0 in specific supply chains and industries (Zekhnini et al., 2020). Further research regarding managements’ attitude and favorable practices related to I4.0 should be analyzed since the existent research is limited (Horváth & Szabó, 2019; Koh et al., 2019). One industry that is currently facing the digitalization phase of I4.0 is the agri-food industry, addressed in the following section.

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2.2.3 I4.0 Technology Adoption in the Agri-food Industry

The decision of adopting new technologies is affected by capabilities of the organization, managerial cognition and environmental incentives (Annosi et al., 2019). Finding the most suitable methods for agri-food supply chains remains an unsolved challenge (Lezoche et al., 2020). Lack of knowledge of decision makers might become a barrier in terms of utilizing the business capabilities and impede the opportunity to take advantage of environmental incentives (Annosi et al., 2019). Among several barriers of decision-making in the food industry, Nazam et al. (2020) points out that managerial barriers are the most critical ones affecting the adoption of knowledge management, which is a challenging process. Furthermore, issues regarding policies and principles of adopting new technologies can be seen as barriers (Long et al., 2016; S. Kumar et al., 2021), which may prevent food companies from meeting future demand. Further research is needed to understand the aspect and reasoning behind the decisions of farmers choice to adopt to Smart Solutions (Annosi et al., 2019), which combine innovative technologies. The factors behind other agri-food players’ decisions to adopt to specific I4.0 technologies are not clearly stated in current research. Thus, Kittipanya-ngam & Tan (2020) highlight a need for more case evidence from different supply chain players, not only focusing on manufacturers, to provide more insights of challenges and opportunities of the digitalization in food supply chains. The implementation of I4.0 in the agri-food industry was explored by Oltra‐Mestre et al. (2021) which conducted research related to Spanish producers. This study suggested that further research of implementation is needed as the insights provided in the study might not be generalized to other countries. Also, their research recognized the economic importance of the European agri-food sector, thus creating a need for further studies of implementation of I4.0 technologies in specific countries. I4.0 technologies are recognized to require a complex and challenging business transformation, but the technical innovations do also favor sustainability (S. Kumar et al., 2021).

The future of the agri-food industry demands a flexible and sustainable farming system (Lezoche et al., 2020). The increased demand from consumers of a more environmentally and sustainable agri-food supply chain requires organizations to implement new

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technologies and focus on circular economy practices (S. Kumar et al., 2021). In the agri-food supply chain, the interest of sustainable sourcing strategies of the focal firm or the company that works towards the end consumers can be highlighted as an important driving force of the upstream implementation of climate smart agriculture technologies (Long et al., 2016). Although, research address the importance of sustainable practices of farming and agriculture companies, there is less research focusing on the other actors in the agri-food supply chains. Besides meeting the consumer demand for more sustainable agri-food supply chains, the I4.0 technologies are recognized to provide precise data.

New disruptive technologies have the possibility to reduce uncertainty by providing precise real-time data which means that businesses have the information tools needed to rapidly adapt and manage changing conditions in the supply chain (Lezoche et al., 2020). The agricultural industry faces challenges of traceability, information asymmetry and trust among supply chain partners (Liu et al., 2020), challenges which BCT can help solving.

2.3 BCT in SCM

In this section, focus is put on discussing BCT in relation to SCM in order to provide understanding of how these two concepts are related as well as what affect the adoption of BCT has on SCM. As BCT can be used in many various areas and is not originally connected to SCM, this section is of high relevance as it specifically discusses the technology in the area of SCM which is the relevant area of use within this thesis.

There has been increased interest in studying BCT within SCM and it has been found to force the field of SCM to evolve new business strategy models (Queiroz et al., 2019) and provides the opportunity to exploit resources and competencies already existing within the supply chain (Hald & Kinra, 2019). Although several definitions of BCT can be found (Queiroz et al., 2019), research in the field of SCM explain it as a distributed digital ledger technology (Kouhizadeh et al., 2021; A. Kumar et al., 2020; Saberi et al., 2019; Wang et al., 2019), in which each block is connected to the block before and after (Kouhizadeh et al., 2021; Wang et al., 2019). When using BCT no single central authority controls the data (Queiroz et al., 2019; Wang et al., 2019), since the technology itself can highlight

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the nature, quality, quantity, location and ownership of a product, hence decreasing a need for central control (Saberi et al., 2019) and instead offering decentralized control (Kouhizadeh et al., 2021). Trackability and traceability are the main success factors within a BCT-based supply chain (Shoaib et al., 2020). Using BCT in SCM not only provides value through increased traceability (Hald & Kinra, 2019), but also through smart contracts, increased supply chain visibility (Gurtu & Johny, 2019; Saberi et al., 2019; A. Kumar et al., 2020; Wang et al., 2019) and improved data security (Saberi et al., 2019; Queiroz et al., 2019; Wang et al., 2019). Also, the technology can increase efficiency as it replaces intermediaries (Kouhizadeh et al., 2021). However, BCT can also introduce several constraints as some of the effects are two-edged (Hald & Kinra, 2019). For example, the increased transparency can also lead to data privacy issues and the increased security makes it difficult to modify data which sometimes cause problems if something was done wrong. Also, the ability to reduce intermediaries can negatively impact certain workers and organizations who lose their part in the supply chain. Moreover, BCT is a high-cost technology (A. Kumar et al., 2020) that requires much computer storage (Hald & Kinra, 2019; A. Kumar et al., 2020). Despite the fast development, BCT is still in its infancy (Kouhizadeh et al., 2021; Shoaib et al., 2020; Wang et al., 2019) and its implementation within SCM is limited (Ghode et al., 2020; Gurtu & Johny, 2019; Pournader et al., 2020). Although the interest of studying BCT in SCM is increasing, Gurtu and Johny (2019) was the first researchers conducting a literature review of the use of BCT in SCM, thus indicating that research is in an early phase. Hence, Ghode et al. (2020) suggests that increased research of challenges of BCT linked to specific product supply chains is needed. Even though research is scarce, some scholars have identified factors affecting adoption and implementation of BCT.

There are several factors that affect the adoption of BCT in SCM. As it is considered to increase trust within the supply chain (Ghode et al., 2020; Gurtu & Johny, 2019; A. Kumar et al., 2020), trust is identified as a primary driver of adoption (Wang et al., 2019). Moreover, organizational, technological (Ghode et al., 2020; Saberi et al., 2019; Wang et al., 2019) and operational challenges (Ghode et al., 2020; Wang et al., 2019) are found to affect the adoption. However, research in this field is still limited and hence suggests that barriers of implementing BCT in SCM need to be further explored (Gurtu & Johny, 2019; Shoaib et al., 2020). Nonetheless, as BCT can be seen as an exploration technology

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enabling innovations for supply chain sustainability (Hald & Kinra, 2019), the concept of Sustainable SCM (SSCM) has gained increased interest.

Adopting BCT in SCM is predicted to have the power to improve economic, social and environmental sustainability (Saberi et al., 2019). However, the extensive energy requirements of a BCT-based supply chain are highlighted as one of its biggest challenges (Gurtu & Johny, 2019; A. Kumar et al., 2020). Using BCT to govern and manage a sustainable supply chain introduces many challenges/barriers (Saberi et al., 2019), the most dominating ones being related to the technology itself (Kouhizadeh et al., 2021). Therefore, initial attention should be dedicated to overcoming the technological barriers to both address organizational difficulties and decrease the supply chain obstacles. Also, supply chain partners need to identify and address the barriers of adopting BCT in order to successfully implement it (Saberi et al., 2019). However, research of both BCT and SSCM is still in an early phase (Kouhizadeh et al., 2021) making it difficult to plan and identify such barriers. As the agri-food supply chains has been showing increased need of a more sustainable supply chain management and hence is recognized of having a need for BCT adoption the next section will address BCT is these specific supply chains.

2.4 BCT Adoption in Agri-food Supply Chains

This section provides an analysis of the current state of research regarding BCT in agri-food supply chains in order to intertwine the main areas of this frame of reference: Technology adoption, SCM, BCT and the Agri-food industry. By doing so, this represents the main section. First, an introduction to the relevance of BCT and its state of research within agri-food supply chains is provided. Secondly, the section continues by focusing on adoption benefits and challenges providing insights about BCT impact on agri-food supply chains and identified benefits and drivers as well as challenges and barriers to adoption.

BCT is a fairly new technology in an immature implementation stage within the agri-food industry and its supply chains (Köhler & Pizzol, 2020; Tiscini et al., 2020; Chen, et al., 2020). It is not considered a stand-alone-technology but rather a technology which needs to be integrated in a system of technologies to work successfully (Köhler & Pizzol, 2020).

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Previous research tends to show that BCT is inadequately tested within the agri-food industry and future impact, outcomes, as well as related issues, are not yet encountered (Köhler & Pizzol, 2020). However, BCT is entering the industry with many expectations and much potential (Chen et al., 2020; Dutta et al., 2020), but knowledge is a critical factor that impacts the adoption. The technology has the possibility to revolutionize and change the conditions and tools within the agri-food supply chains which also allow consumers to be aware of the journey of their food (Bumblauskas et al., 2020). Further, BCT involves high costs which means that an accurate analysis is required to make sure that potential benefits will outweigh those costs (A. Kumar et al., 2020). The need for BCT is especially important among industries, such as the agri-food industry, where consumers demand more transparency and secure information regarding products and processes (Tiscini et al., 2020). However, as Sarkis et al. (2020) highlights that academic research in the field of digitalization and greening of supply chains is leading industry practice, one can conclude that increased academic research is needed to guide practice. Even though it is a fairly young technology it can be seen as a solution to certain challenges faced in agri-food supply chains.

The agri-food industry has demanding consumers that are concerned about food safety in terms of for example quality, transparency, and ability to trace product history (Bumblauskas et al., 2020; Casino et al., 2020; Tiscini et al., 2020). Consumer demand is in many cases an important factor that impacts the likelihood of BCT adoption within the agri-food industry (Bumblauskas et al., 2020). Kamble et al. (2020) argue that there is a lack of labeling practices that provide and ensure consumers with information about the products they are buying. Moreover, the agri-food industry does also deal with complex, often global, supply chains with several intermediaries that makes it challenging to control the journey of the products (Dutta et al., 2020; Kittipanya-ngam & Tan, 2020). BCT is a solution to several of the issues within the agri-food industry since it supports digitization of supply chains (Köhler & Pizzol, 2020) by securing data as well as reducing issues related to intermediaries while at the same time enhancing trust to stakeholders (Tiscini et al., 2020). BCT allow for better management of important data that flows between partners in the supply chain where lack of trust and provenance is a current issue (A. Kumar et al., 2020). Overall, BCT implementation in agri-food supply chains is related to several benefits and opportunities as well as challenges and barriers (Feng et

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al., 2020; Köhler & Pizzol, 2020; Zhao et al., 2019). Even though BCT can be considered a promising technology (Hughes et al., 2019), there is still a need for further implementation and theoretical explanations of successful attempts as well as failures to encourage the development of BCT-enabled supply chain systems (Nandi et al., 2020). Although BCT can be seen as a solution for many challenges, the barriers and opportunities can differ depending on the context of the adoption.

Companies possessing a dominant position in the supply chain are expected to be more likely to take initiative to adopt BCT in food supply chains (Chen et al., 2020). Digitized food supply chains and their adoption processes might also be influenced by different structures depending on which country it operates in, while the role of the company for example farmer, retailer, or distributor might face different barriers and opportunities when digitizing the supply chain (Kittipanya-ngam & Tan, 2020). Kamble et al. (2020) points out the importance of finding BCT enablers that are significant concerning specific countries and what impact they have since the technical practices and conditions within countries might differ. Further, disruptive technologies such as BCT are transforming the traditional agri-food industry. However, what impact BCT has on specific supply chains remains unclear, and the practices required when adopting BCT as well as the expected result is dependent on the industry in which it is adopted (Nandi et al., 2020).

The following section will analyze the most highlighted and discussed benefits, drivers, challenges and barriers that companies in the agri-food industry might encounter when adopting BCT. In the literature search for this thesis, no academic research of BCT adoption specific to the Swedish agri-food industry could be found. Thus, one should keep in mind that the following information is highlighted by various researchers, researching different markets, hence not specifically studying Swedish agri-food supply chains. However, to provide an understanding of the state of current literature in the field, the findings of these studies are highly relevant to review.

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2.4.1 Adoption Benefits & Drivers

By reviewing seven academic articles in the field of BCT adoption in the agri-food industry, certain benefits of adoption and the factors driving adoption were found.

Two of the most highlighted benefits and effects of BCT adoption in agri-food supply chains is increased traceability and transparency. Adopting BCT increases traceability within the supply chain (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Zhao et al., 2019). This benefit is, according to the analysis of six various cases in the agri-food industry conducted by Köhler and Pizzol (2020), directly connected to the attributes of BCT. In order to achieve traceability, a robust, secured and sharable technology platform which is decentralized and enabled by smart contracts is needed (Kamble et al., 2020), namely BCT. Traceability is not only pointed out as a main benefit of BCT, but also a primary driver and enabler of adoption (Kamble et al., 2020; Saurabh & Dey, 2021), thus a factor that seems to be of high importance for agri-food companies to be able to ensure safe and high-quality food which creates a positive brand image and increases trust (Kamble et al., 2020). BCT enables collection of real-time data of everything from the production, processing, storage, distribution and retailing of food which enhances traceability (Kamble et al., 2020) and makes it possible to provide an overview of the whole supply chain, something which is most often not possible without BCT (Köhler & Pizzol, 2020). Because of this, customers can for example be part of the control and health risk prevention if there would be any failure or risk connected to a production batch (Tiscini et al., 2020). Being able to trace all transactions in a supply chain further makes it possible to provide increased transparency, another benefit of BCT (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020; Zhao et al., 2019). Just as traceability, transparency is identified as an impact that is directly connected to the attributes of the technology (Köhler & Pizzol, 2020). By adopting BCT the transparency of operating processes such as traceability management is increased (Feng et al., 2020; Tiscini et al., 2020) while also enhancing the transparency of product quality (Tiscini et al., 2020). Transparency among other factors introduced by BCT, has been found to increase food safety and quality (Tiscini et al., 2020).

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Adopting BCT in an agri-food supply chain will enhance food safety (Tiscini et al., 2020; Zhao et al., 2019) and food quality (Chen et al., 2020; Tiscini et al., 2020; Zhao et al., 2019). By doing so. it is possible to reach a high level of social sustainability while also allow customers to develop increased awareness of the products they are consuming (Tiscini et al., 2020). Together with accountability and verifiability, traceability has also been found to enhance trust and efficiency within agri-food supply chains.

Trust and reliability are benefits of BCT (Chen et al., 2020; Feng et al., 2020; Zhao et al., 2019) which according to Köhler and Pizzol (2020) represents direct impacts of adoption. One example of how trust can be enhanced through BCT is through the ability to provide customers with direct and immutable data which they obtain through scanning a QR code printed on the product (Tiscini et al., 2020). Similarly to traceability, trust is also found to be a driver of adoption (Saurabh & Dey, 2021), meaning that agri-food companies adopt BCT with the goal to increase trust as it has been shown to do so both among consumers and the public (Tiscini et al., 2020).

Adopting BCT in an agri-food supply chain also increases the supply chain efficiency (Chen et al., 2020; Feng et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020). However, the increased efficiency is an indirect impact of BCT adoption (Köhler & Pizzol, 2020), hence is not directly connected to the technology itself. Together, traceability and transparency are found to be two benefits of BCT adoption which increases supply chain efficiency (Feng et al., 2020). By adopting BCT, enhanced efficiency in business transactions is enabled through smart contracts, a benefit which can be favorable for partners in the supply chain as it facilitates trade (Tiscini et al., 2020). With the help of smart-contracts and cost-effectiveness, increased economic value is provided and transaction costs are reduced (Feng et al., 2020). Besides smart contracts, disintermediation is another effect of BCT adoption (Kamble et al., 2020) which is the most important adoption driver when studying the Indian wine industry (Saurabh & Dey, 2021). For example, dis-intermediation contributes to lower costs, something that companies value since price is a factor which influences the adoption decision. Dis-intermediation is expected and found to reduce inefficiencies and delays of transactions (Kamble et al., 2020; Tiscini et al., 2020) as well as provide greater financial inclusion, reduce risk (Tiscini et al., 2020) and high transaction lead times (Kamble et al., 2020).

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While trust and efficiency are enhanced through accountability, verifiability and traceability (Tiscini et al., 2020), the increased transparency, traceability and trust is a result of the immutability and decentralization introduced by BCT (Köhler & Pizzol, 2020). Using BCT in agri-food supply chains increases security of information (Feng et al., 2020; Tiscini et al., 2020; Zhao et al., 2019), a benefit related to immutability, which is another benefit (Zhao et al., 2019) and enabler of BCT adoption (Kamble et al., 2020). Immutability is described as an indirect impact of BCT adoption which reduces risk of fraud and corruption (Köhler & Pizzol, 2020; Tiscini et al., 2020) and contributes to increased public safety (Tiscini et al., 2020). However, Köhler and Pizzol (2020) claim that no strong evidence of its effect on anti-corruption can be found yet. Along with immutability, control is also considered to be a driving force of adoption (Saurabh & Dey, 2021). By providing more accurate information about the food, BCT increases control (Köhler & Pizzol, 2020) of, for example, risks and quality.

Based on the characteristics of BCT, another indirect impact of adoption is related to sustainability (Köhler & Pizzol, 2020). Benefits of BCT adoption can improve sustainability management (Feng et al., 2020) and the sustainability performance of the agri-food supply chains (Kamble et al., 2020) in many various ways (Tiscini et al., 2020). For example, it can help to track sustainability certificates (Köhler & Pizzol, 2020). Thus, BCT has been found to be a source of sustainable innovation as it can help to achieve several SDGs connected to the society, economy and environment (Tiscini et al., 2020). However, because of the immature implementation stage Köhler and Pizzol (2020) claims that no strong evidence of BCT adoption’s effect on increased sustainability exists yet.

Generally, studies focus mostly on the benefits of BCT adoption (Kamble et al., 2020; Tiscini et al., 2020). However, many beneficial factors are found to be two-edged providing both benefits and challenges (Tiscini et al., 2020). Hence researchers suggests that barriers of implementing BCT in SCM need to be further explored (Gurtu & Johny, 2019; Shoaib et al., 2020). The next section will provide an analysis of the previous literature findings regarding challenges and barriers related to the adoption of BCT in the agri-food industry.

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2.4.2 Adoption Challenges & Barriers

There is a general immaturity of the application of BCT (Chen et al., 2020) which is related to challenges regarding user acceptance, supportive business processes, technologies and applications as well as labor and skill shortage and low familiarity and misunderstandings of BCT adoption. Related to its newness, the lack of skills, trust (Feng et al., 2020) and knowledge of early adopters is one of the challenges of adopting BCT in the agri-food industry (Tiscini et al., 2020; Zhao et al., 2019). Besides this knowledge gap there are many more challenges, some of them being connected to the technology itself.

Adopting BCT within agri-food companies introduces technical challenges (Feng et al., 2020; Kamble et al., 2020) related to the characteristics of the technology (Chen et al., 2020). For example, BCT involves a challenge of latency (Feng et al., 2020; Zhao et al., 2019) which is linked to its transaction capability (Chen et al., 2020). Currently, BCT-enabled systems need time to process transactions and the capacity cannot process millions of transactions in real-time (Zhao et al., 2019). Since BCT only has the capacity to process a few transactions per second, an issue of scalability is introduced (Chen et al., 2020; Feng et al., 2020; Zhao et al., 2019). An issue which has not yet been solved due to the newness of the technology and other challenges such as data storage capacity (Feng et al., 2020; Zhao et al., 2019). Another identified technical challenge is security requirements related to the protection of privacy (Chen et al., 2020; Feng et al., 2020). This is an issue connected to the two-edgeness of transparency as it on one hand is beneficial but on the other hand can increase the risk of privacy leakage (Zhao et al., 2019). Not only transparency is two-edged, also immutability. As well as being beneficial, the immutability of data within BCT creates an error intolerance (Chen et al., 2020) as data cannot be changed (Köhler & Pizzol, 2020). This can be problematic since there is no guarantee that the data first entered to the chain is reliable or of good quality (Chen et al., 2020; Köhler & Pizzol, 2020).

Further, BCT is an energy-intensive technology (Feng et al., 2020) which requires extensive computing power as well as high bandwidth internet (Kamble et al., 2020). Linked to the enormous amount of electricity needed (Zhao et al., 2019) among other factors, BCT adoption requires high financial investments (Chen et al., 2020; Tiscini et

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al., 2020; Zhao et al., 2019). For example, the lack of knowledge regarding BCT implementation introduces employee training costs (Tiscini et al., 2020).

As highlighted by Chen et al. (2020), there is a general complexity of BCT integration in the agri-food industry which is linked to various challenges of having multiple datasets from all different ingredients, the need for industrial cluster adoption, a common and open data standard, legacy systems and databases as well as integration of different platforms.

Just as Chen et al. (2020) mention, adopting BCT involves institutional challenges (Feng et al., 2020; Kamble et al., 2020) which primarily includes legal and regulatory issues (Feng et al., 2020; Chen et al., 2020; Kamble et al., 2020; Zhao et al., 2019). Currently, there is a lack of regulations and standards of BCT implementation (Chen et al., 2020; Feng et al., 2020) both on a national and global level (Chen et al., 2020). This absence of regulation and standards is a problem as BCT is such a new technology involving many different parties from various countries (Zhao et al., 2019) thus creating a need for unified regulations and standards. There is also a lack of standardization and flexibility of BCT architecture which restricts the interoperability between technology solutions (Feng et al., 2020). To achieve interoperability, all stakeholders need to collaborate (Feng et al., 2020), thus substantial benefits of BCT is dependent on supplier engagement (Chen et al., 2020; Köhler & Pizzol, 2020; Tiscini et al., 2020), which sometimes is difficult in cases where suppliers are small and may not be able to pay for the implementation (Tiscini et al., 2020).

Although some scholars have conducted research related to the challenges of adopting BCT in the agri-food supply chains, the amount of research focusing on it is still scarce. Both Kamble et al. (2020) and Queiroz et al. (2019) highlight that the process of BCT adoption is dependent on which country that is considered. Hence, Queiroz et al. (2019) presents a need for further research of BCT adoption related to specific countries, industries and businesses while Kamble et al. (2020) highlight the importance of identifying enablers of adoption related to specific country conditions. Further Kamble et al. (2020) suggests that future studies in this topic should be conducted in developed economies due to their technologically advanced agricultural practices which can contribute to new knowledge.

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3. Methodology & Method

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The following chapter explains how the research is conducted. It will consist of three different main parts. The first one is the methodology that highlights the chosen research paradigm, research approach and research design. Secondly, a method section which focuses on how data is collected and analyzed is presented followed by an ethical analysis of the thesis.

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3.1 Methodology

3.1.1 Research Paradigm

A research paradigm refers to a philosophical framework that is based on people's beliefs and thoughts about the reality and nature of knowledge to guide research structure (Collis & Hussey, 2014). Among several there are two main paradigms: positivism and interpretivism, that separate natural and social science (Bryman, 2016; Collis & Hussey, 2014). The interpretative paradigm is based on the belief that social reality is subjective, multiple and in our minds. As this thesis aims to analyze the reasoning behind Swedish agri-food organizations’ decisions to adopt or reject BCT as well as their approach to adopting BCT in their supply chains the thesis will investigate and explore the context and perceptions of individuals in order to understand the complexity behind the adoption process. Hence, whenconducting this thesis, the understanding of humans’ roles as social actors was of high importance to see how the individuals differ and interpret the complexity of the phenomena (Saunders et al., 2016). Thus, this research is based on an interpretivist paradigm which is suitable as it is rather subjective than objective meaning that findings will be drawn on individual perceptions and opinions. Using this paradigm allows the thesis to derive rich and qualitative data from small samples to develop an interpretative understanding of the phenomena of BCT adoption in the specific context of the Swedish agri-food industry, thus providing patterns and theories with high verification (Collis & Hussey, 2014). Furthermore, when conducting business and management research of organizational behavior, such as this thesis, an interpretivism perspective is highly appropriate due to the complexity and uniqueness of each business situation (Saunders et al., 2016).

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3.1.2 Research Approach

When considering the logic of the research there are two main directions that can be followed, either inductive or deductive (Collis & Hussey, 2014). In general, there is an assumption that studies with an interpretivism paradigm choose an inductive approach. This approach is a simple and systematic way of analyzing qualitative data which is considered to generate both reliable and valid findings (Thomas, 2006). Since the thesis focuses on developing theory from empirical social reality and transforms specific observations into general patterns, an inductive approach is used. The purpose of using an inductive approach is to enable the creation of a framework of BCT adoption from the raw data (Thomas, 2006). Thus, the empirical observations of employees’ and experienced individuals’ perceptions of BCT adoption has the aim to derive general patterns which can build theories and provide current literature with new generalizations about BCT adoption in the Swedish agri-food industry. By doing so, the thesis explores the previously researched phenomena of BCT in a new context by specifically focusing on the Swedish agri-food industry, something which Gabriel (2013) considers as one type of inductive focus.

3.1.3 Research Design

When categorizing a research, different characteristics are considered, for example the process which describes the data and the way it is gathered and analyzed (Collis & Hussey, 2014). The research can either have a quantitative approach where data is analyzed through statistical methods or a qualitative approach where the data is analyzed through interpretative methods. This means that a qualitative research design if often associated with the interpretative paradigm (Creswell, 2014) as well as an inductive approach (Gabriel, 2013). Hence, this thesis will take on a qualitative approach where the purpose is to answer the research questions through a deeper understanding of BCT adoption, the research phenomena. Using a qualitative method allow the thesis to do so by collecting accurate data and gaining new insights of the phenomena (Saunders et al., 2016). This will be done through multiple interviews with different organizations as well as experienced individuals within the industry, where the phenomena will be analyzed to derive new generalizations and patterns.

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3.2.1 Primary Data

Primary data is data provided from an initial source which means that it is collected by the researchers own experiments, surveys, interviews or focus groups (Collis & Hussey, 2014). Interviews are associated with the interpretivist paradigm, which is favorable since the goal of this thesis is to gain a deeper understanding of the phenomena that is studied, namely BCT adoption. This thesis collected primary data through 9 semi-structured interviews with 8 organizations possessing relevant knowledge and insights. Using semi-structed interviews as the data collection method is common when conducting qualitative research (Saunders et al., 2016). As this method grant the authors to conduct partly structured interviews which still leaves space for modifications, it is the preferable method for this thesis and thus enables the interviews to be personalized and let the interviewee participants provide answers about what they think, do or feel.

3.2.2 Sampling Approach

A sample is a subgroup of a population which is a group of people with similar characteristics that needs to be selected when collecting data for the study (Collis & Hussey, 2014). As confirmed earlier, an interpretivist paradigm is used in this thesis, which means that no random sample is needed since the data does not need to be analyzed statistically as it should not generalize the population (Collis & Hussey, 2014). Instead, a non-random sampling method is needed in order to find an applicable sample for the thesis. The population in this case is all organizations that possess knowledge and/or experience of BCT adoption. However, in order to find a suitable sample, a snowball sampling approach was used in order to reach suitable organizations and people. To do so, the researchers started networking and conducted shorter interviews to determine if the person and organization was relevant or not. Also, relevant contacts were shared, which agrees with how a snowball sample is selected (Collis & Hussey, 2014). In the beginning it was difficult to find suitable organizations but eventually the researchers encountered organizations through people with deeper knowledge about the industry and the field of technology. However, to find the best possible sample a purposive sampling method, commonly used in qualitative research, was chosen (Chamberlain & Hodgetts,

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2018; Kaczynski et al., 2014). This means that the individuals chosen in the sample are considered to possess rich information of the topic and can therefore contribute to findings that help answer the research questions. In this case, it means that the experience of the phenomena was considered as a critical criterion when the interviewees were selected. The purposive sampling method was chosen since the thesis has a niche topic that requires organizations to have prior knowledge or experience of the technology, which is difficult to find since there are only few Swedish agri-food organizations that have knowledge or experience of BCT adoption. Thus, organizations possessing this knowledge can be considered a hard-to-reach group which according to Chamberlain and Hodgetts (2018) can successfully be reached through a purposive sampling method. The sample of this thesis contains of 8 organizations were 5 of them represent an agri-food organization in which BCT adoption could be of interest (see table 2) and the additional 3 are referred to as experienced individuals in the field of agri-food and technology adoption (see table 3).

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Table 3: Description and relevancy of the organizations of the 3 experiences

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3.2.3 Semi-structured Interviews

The primary data was collected through semi-structured interviews which means that somequestions were prepared before the interviews in order to encourage and guide the interviewee but at the same time remain focused on the topic. As opposed to a positivist interview, no specific structure was needed, which allowed the conversation to elaborate further, and new questions to be developed during the interview (Collis & Hussey, 2014). Semi-structured interviews are a good alternative to gather primary data in a qualitative study with the chosen sample method (McIntosh & Morse, 2015). Also, this interview form is suitable for an interpretivist study since it allows the researchers to get in-depth answers through a flexible structure (Collis & Hussey, 2014).

Due to the situation of the pandemic, Covid-19, the interviews were held online through Microsoft Teams. This entails both disadvantages and advantages, since it would be more favorable to conduct the interviews face to face as it is easier to observe reactions and body language (McIntosh & Morse, 2015). On the contrary, the online interview format enabled interviews to be conducted regardless of the interviewee’s location, which was favorable due to the geographical spread of interview organizations.

Both researchers attended the interviews in order to avoid mistakes and to ensure that the required information was gathered. The duration of each interview was between 35 to 60 minutes and were conducted in the interviewees’ native language, Swedish, in order to make sure that the interviewee felt comfortable and able to answer the questions naturally to avoid misunderstandings. However, a translation of the written quotes was completed, a process which call for caution as it can negatively affect the quality and validity of the findings (Birbili, 2000). Thus, to make sure that translations were expressing the same content as what had been said in the interview and ensure the quality and validity of the translations, the quotes were first translated separately by both authors to then, based on the two suggestions, form the final version of the quote.

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3.2.4 Interview Questions

Within the semi-structured interviews different types of questions were asked. There are three main categories of questions that can be used; (1) open, (2) probing and (3) specific and closed questions (Saunders et al., 2016). The majority of the questions used were open-ended in order to ensure that answers would provide deeper insights and understanding of perceptions regarding BCT adoption. This is easier to achieve with open-ended questions since the interviewee needs to consider and reflect over their answers (Collis & Hussey, 2014). Moreover, some probe questions were used to get an improved understanding of the already provided answers as well as few specific closed questions were asked to get a clear answer of facts and opinions, i.e., if the organization uses blockchains right now.

Regarding the interviews, some questions were prepared in advance. Thus, 12 main questions were prepared for the agri-food organizations (see appendix 1) and 8 slightly different main questions were prepared for the experienced individuals (see appendix 2). The prepared questions were based on the gathered literature presented in the frame of reference and the proposed research questions. The use of semi-structured interviews allowed the authors to gain in-depth answers of the interviewees’ perceptions regarding BCT adoption by conducting interviews one and one, utilizing open-ended questioning, using inductive probing and appearing like a conversation, what Guest et al. (2013) consider the features of an in-depth interview. In that way it was possible to provide an in-depth analysis of the reasoning behind Swedish agri-food organizations’ decisions to adopt or reject BCT and their adoption approach.

3.2.5 Data Analysis

Qualitative research often involves a large amount of data, meaning that the analysis of qualitative data often is more challenging and time-consuming if compared with quantitative (Collis & Hussey, 2014). The data analysis of this qualitative thesis was executed through a thematic analysis, a commonly used coding method for qualitative studies (Braun & Clarke, 2006). Using this method allowed the authors to manage and interpret the large amount of data with flexibility by identifying, analyzing and presenting themes developed from the empirical data. Before starting with the coding all transcripts