Degree Project

Big Data Analytics for Agriculture

Input Supply Chain in Ethiopia: Supply Chain Management Professionals Perspective

Author: Abdurahman Alewi Hassen

Author: Bowen Chen Supervisor: Assistant Professor, Niclas Eberhagen

Examiner: Associate Professor Päivi Jokela Date: 2020-05-26

Course Code:5IK50E, 30 credits

Subject: Information Systems

Level: Graduate

Department of Informatics

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Abstract:

In Ethiopia, agriculture accounts for 85% of the total employment, and the country’s export

entirely relies on agricultural commodities. The country is continuously affected by chronic food

shortage. In the last 40 years, the country’s population have almost tripled; and more agricultural

productivity is required to support the livelihood of millions of citizens. As reported by various

research, Ethiopia needs to address a number of policy and strategic priorities to improve

agriculture; however, in-efficient agriculture supply chain for the supply of input is identified as

one of the significant challenges to develop agricultural productivity in the country.

The research problem that interest this thesis is to understand Big Data Analytics’ (BDA) potential

in achieving better Agriculture Input Supply Chain in Ethiopia. Based on this, we conducted a

basic qualitative study to understand the expectations of Supply Chain Management (SCM)

professionals, the requirements for the potential applications of Big Data Analytics - and the

implications of applying the same from the perspectives of SCM professionals in Ethiopia. The

findings of the study suggest that BDA may bring operational and strategic benefit to agriculture

input supply chain in Ethiopia, and the application of BDA may have positive implication to

agricultural productivity and food security in the country. The findings of this study are not

generalizable beyond the participants interviewed.

Keywords:

Agriculture, Big Data, Big Data Analytics, Supply Chain, Supply Chain Management,

Agriculture Supply Chai, Agriculture Input Supply Chain, Food Security, Sustainability

Acknowledgements:

This study is conducted to complete our Master’s studies in Information systems at Linnaeus

University. We want to acknowledge the assistance of our professors, classmates and colleagues

who made this thesis possible.

Specifically, we would like to thank our supervisor Assistant Professor Niclas Eberhagen, for his

guidance throughout the entire thesis process. And, we would like to thank Professor Anita

Mirijamdotter and Associate Professor Päivi Jokela, for their feedback and encouragement.

We would also like to express our sincere appreciation to all participants for their valuable time

and contribution to the successful completion of this study. Finally, we would like to thank our

families for their love and support.

Thank You.

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Table of Contents

1. INTRODUCTION 5

1.1 INTRODUCTION AND RESEARCH SETTING 5 1.1.1 Background 5 1.1.2 BDA in ASC: General Introduction 6 1.1.3 BDA in ASC: Studies in Ethiopia 7

1.2 PURPOSE STATEMENT AND RESEARCH QUESTIONS 7 1.2.1 Purpose Statement 7 1.2.2 Research Questions 7

1.3 TOPIC JUSTIFICATION 8 1.4 SCOPE AND LIMITATIONS 8 1.5 THESIS ORGANIZATION 8

2 REVIEW OF THE LITERATURE 10

2.1 LITERATURE SEARCH PROCESS 10 2.1.1 Inputs - Literature collection and screening process 10 2.1.2 Processing - Based on bloom's taxonomy 10 2.1.3 Output - Writing the final literature review 10

2.2 BASIC CONCEPTS 11 2.2.1 Big Data 11 2.2.2 Big Data Analytics 11 2.2.3 Big Data in Supply Chain Management 11 2.2.4 Big Data Analytics in Agriculture Supply Chain 12

2.2.4.1 Social, environmental and economic aspects 12 2.2.4.2 Big data Analytics applications in the Agriculture Supply Chain Process 13 2.2.4.3 Analysis in Supply Chain Management: 14

2.2.4.3.1 Descriptive analytics: 15 2.2.4.3.2 Predictive analytics: 15 2.2.4.3.3 Prescriptive analytics 15

2.2.5 Risks and Challenges of Big Data Analytics in Agriculture Supply Chain 15 2.2.5.1 Monopoly and Role Change 15 2.2.5.2 Ethics and Privacy 16 2.2.5.3 Barriers 17

2.3 THEORETICAL FRAMEWORK 17 2.3.3 BDA and RBV 18 2.3.4 BDA, SCM and RBV 18 2.3.5 RBV in this study context 19

3 METHODOLOGY 20

3.1 METHODOLOGICAL TRADITION 20 3.2 METHODOLOGICAL APPROACH 21 3.3 DATA COLLECTION METHODS 22

3.3.1 Interviews – Primary data 22 3.3.2 The participants 23 3.3.3 The Interview session 25 3.3.4 Documents – Secondary data 25

3.4 DATA ANALYSIS METHOD 25 3.5 ANTICIPATED RISKS 26 3.6 RELIABILITY AND VALIDITY 27 3.7 ETHICAL CONSIDERATIONS 28

4 EMPIRICAL FINDINGS 30

4.1 DOCUMENT ANALYSIS 30 4.1.1 Description of documents included in the analysis process 30 4.1.2 What are the expectations in the application of BDA in AISC? 31 4.1.3 What are the requirements for applying BDA in AISC? 32

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4.1.5 Summary of document analysis 34 4.2 INTERVIEW DATA ANALYSIS 35

4.2.1 What are the expectations of the participants in the application of BDA in AISC? 35 4.2.1.1 Supply Chain Visibility 36

4.2.1.1.1 Transparency in the input supply chain 36 4.2.1.1.2 Traceability in the input supply chain 36 4.2.1.1.3 Counterfeit agricultural inputs 37 4.2.1.1.4 Short Supply chain 37

4.2.1.2 Analytics 37 4.2.1.2.1 Descriptive – reporting trend 37 4.2.1.2.2 Predictive – future demand 38 4.2.1.2.3 Prescriptive – next step / action 38

4.2.1.3 SCM functions 38 4.2.1.3.1 Procurement management function 38 4.2.1.3.2 Warehouse and transport management functions 39

4.2.2 What are the requirements for applying BDA in AISC? 39 4.2.2.2 Human 40 4.2.2.3 Intangibles 40 4.2.2.4 Technological 41 4.2.2.5 Financial 41 4.2.2.6 Physical 41

4.2.3 What are the potential implications of applying BDA in AISC? 42 4.2.3.2 Economy 42 4.2.3.3 Social 43 4.2.3.4 Environment 43

4.2.4 Summary of interview analysis 44

5 DISCUSSION 45

5.1 WHAT ARE THE EXPECTATIONS OF THE PARTICIPANTS IN THE APPLICATION OF BDA IN AISC? 45 5.1.1 Supply chain visibility 45

5.1.1.1 Traceability in the input supply 45 5.1.1.2 Counterfeit agricultural inputs 46 5.1.1.3 Short Supply Chain 46 5.1.1.4 Transparency in the input supply 46

5.1.2 Analytics and decision making 47 5.1.3 Support to Supply chain functional areas 47

5.2 WHAT ARE THE REQUIREMENTS FOR THE APPLYING OF BDA IN AISC? 47 5.2.1 Human 48 5.2.2 Intangibles 48 5.2.3 Technological 48 5.2.4 Financial 49 5.2.5 Physical 49

5.3 WHAT ARE THE POTENTIAL IMPLICATIONS OF APPLYING BDA IN AISC? 49

6 CONCLUSION 51

6.1 CONCLUSION 51 6.2 LIMITATIONS 52 6.3 CONTRIBUTION 52 6.4 FUTURE RESEARCH 52 6.3 AUTHOR’S CONTRIBUTIONS 53 6.4 PERSONAL REFLECTIONS 53

6.4.1 Abdurahman Alewi Hassen 53 6.4.2 Bowen Chen 53

7 REFERENCES 54

8 APPENDIX 1 – PARTICIPANT INFORMATION SHEET 62

9 APPENDIX 2 – PARTICIPANT CONSENT FORM 64

10 APPENDIX 3 – INTERVIEW GUIDE 66

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1. Introduction

In this section, we introduce the reader to the topic of interest by presenting background

information about the study topic, the problem area and the study setting. Then the purpose

statement, the research questions, topic justification, scope and limitation will be presented. And

we conclude this section by presenting the organization of the thesis.

Abbreviations: BD – Big Data, BDA – Big Data Analytics, Ag – Agriculture, SC – Supply Chain,

AISC – Agriculture Input Supply Chain, ASC – Agriculture Supply Chain SCM – Supply Chain

Management,

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1.1 Introduction and Research Setting

1.1.1 Background

In Ethiopia, agriculture accounts for 85% of the total employment, and the country’s export

entirely relies on agricultural commodities (FAO, 2020). The country is continuously affected by

chronic food shortage (WFP, 2020). In the last 40 years, the country’s population have almost

tripled; and more agricultural productivity is required to support the livelihood of 112 million

population in the country (World Bank, 2019; Feed the future, 2019). As reported by various

research, Ethiopia needs to address a number policy and strategic priorities to improve agriculture;

however, inefficient agriculture supply chain for the supply of input is identified as one of the

significant challenges to develop agriculture productivity in the country (Feed the future, 2019;

Minot et al., 2019; Tefera, Demeke and Kayitakire, 2017; Agbahey, Grethe and Negatu, 2015).

For example, Feed the Future emphasize the importance of efficient agriculture supply chain not

only for boosting productivity but also for the control of counterfeit products and their negative

consequence on farmers motivation to adopt newly-improved high-quality input (Feed the future,

2019). Similarly, Tefera, Demeke and Kayitakire (2017) highlight the impact of improving the

supply chain to enhance resilience on food security. In the same way, Agbahey, Grethe and Negatu

(2015) related improvement in the supply chain with efficiency gains such as reduced cost of stock

and consequent reduction in the price of fertilizer. Furthermore, Minot et al. (2019) stated the

importance of streamlining seed input supply channels and applying location-specific

recommendation systems for the supply of fertilizers to improve productivity in agriculture. In

summary, all the points mentioned above reflect the need for innovative and efficient data-driven

agriculture supply chain to improve productivity in the country.

Several articles reported that transparency, trust, commitment, visibility and traceability are the

most crucial elements for the realization of efficient ASC (Kamble, Gunasekaran, and Gawankar,

2020). In recent years there has been a broad interest to use emerging technologies as a tool to

manage ASC. In particular, Big Data Analytics (BDA) has become a central point to understand

aspects of the supply chain and increase efficiency and productivity. BDA is currently being

applied to improve agriculture supply chain in developed countries, and it is also considered that

the technology could be applied to improve the same in developing nations (Fleming et al., 2018;

Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017; Wolfert et al., 2017).

Big Data Analytics (BDA) in the ASC is being used to improve efficiency and productivity;

however, many social, ethical and technical issues on the application of BDA in ASC remain a

challenge (Belaud et al., 2019; Kamble, Gunasekaran, and Gawankar, 2020; Singh et al., 2018;

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Lioutas et al., 2019, Fleming et al., 2018; Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017;

Wolfert et al., 2017). As suggested by quite several researchers, a better understanding of the

motivations and expectations of the participants is a critical condition for the application of Big

Data in ASC (Fleming et al., 2018; Jakku et al., 2019; Lioutas et al., 2019)

To conclude this section, the research problem that interest this thesis is to understand BDA’s

potential in achieving better ASC in Ethiopia. For this to be achieved, it is critical to establish a

better understanding concerning the perspectives of the participants into BDA ASC in the study

setting. As we know it, SCM professionals are the key stakeholders in the supply chain, whose

primary objective is to effectively manage diverse resources for the realization of sustainable

competitive advantage. Using a resource-based view, this research intends to understand the

expectations of participants, the requirements and implications of BDA in ASC from the

perspectives of participants.

1.1.2 BDA in ASC: General Introduction

BDA in agriculture can be understood as a holistic approach to analytics in agriculture, which

combine weather data, farm-level data, social media data, and market supply and demand data - in

order to generate insight and actionable knowledge to aid data-driven decision making across the

entire agriculture supply chain (Fleming et al., 2018; Kamilaris, Kartakoullis and Prenafeta-Boldú

2017; Lioutas et al., 2019). As stated by Bronson and Knezevic (2016) the value chain for Big

Data in agriculture includes from farm input suppliers (i.e. fertilizers, chemicals) to technology

service providers – each collaborating to drive value from information. Similarly, Ceislik et al.

(2018) expressed BDA’s potential in terms of boosting collaboration between the various

stakeholders across the agriculture supply chain. Additionally, Kamble, Gunasekaran, and

Gawankar (2020) assessed BDA and emerging technologies in agriculture supply chain

mentioning their significance in achieving a balance between economic growth, environmental

protection, and social development – in line with sustainable development goal. In the same vein,

Belaud et al. (2019) stretched BDA in agriculture application into the management of farm by-

products - and generating value from agricultural waste in order to improve sustainability along

the supply chain. Generally, BDA is stated as a revolution that enables the agriculture supply chain

to become data-driven and demand-oriented - increasing efficiency, productivity, improving food

security and reducing environmental impacts of agriculture (Lioutas et al., 2019; Kamilaris,

Kartakoullis and Prenafeta-Boldú 2017; Fleming et al., 2018; Wolfert et al., 2017).

Several studies recently published on BDA in ASC - focusing on agri-food supply chain visibility

and sustainability (Kamble, Gunasekaran, and Gawankar, 2020), carbon footprint and supplier

selection (Singh et al., 2018), sustainability and Agriculture by-product management (Belaud et al.,

2019) and Agriculture supply chains GIS analytics (Sharma, Kamble and Gunasekaran, 2018) -

and have provided essential concepts and recommendations on how to improve productivity and

efficiency. Beyond productivity and efficiency, more recent articles focused on sustainability

benefits of BDA in ASC (Allaoui et al., 2018; Kamble, Gunasekaran and Gawankar, 2020).

As revealed by quite several existing research on BDA in the supply chain, it is critical to

understand how BDA interplay with the various supply chain variables in the context, as to help

drive real value from the service. Understanding the variables requires addressing environmental,

social, technological and economic factors in the context (Kamble, Gunasekaran and Gawankar,

2020). Notably, it is critical to understand how to achieve coordination among the members of the

supply chain network (Papadopoulos et al., 2017, Kamble, Gunasekaran and Gawankar, 2020),

how to use BDA for Supply chain sustainability initiatives (Hazen et al., 2016), how to improve

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supply chain performance (Gunasekaran, 2017), how unstructured data can be used and create

value in supply chain processes and network (Chen, Preston and Swink, 2015; Papadopoulos et

al., 2017) how to achieve traceability in the stream -up and down- of the supply chain (Zhu et al.,

2018) and what technology, infrastructure and resources required to drive the supply chain network

(Zhong et al., 2016), issues of data quality (Hazen et al., 2014) and the knowledge and skills

required for data analytics (Wang et al., 2016, Waller and Fawcett., 2013).

1.1.3 BDA in ASC: Studies in Ethiopia

It is widely believed that the adoption of BDA will enable developing countries AISC to be more

efficient, productive and sustainable (Kamble, Gunasekaran and Gawankar, 2020; Kamilaris,

Kartakoullis and Prenafeta-Boldú, 2017; Wolfert et al., 2017). However, for this to be achieved

the motivation and expectations of stakeholders in the context need to be understood (Kamble,

Gunasekaran and Gawankar, 2020; Lioutas et al., 2019; Fleming et al. ,2018; Jakkua et al. 2019).

However, BDA in AISC is a new phenomenon, and the available studies on the area addressed

mostly developed countries (Kamble, Gunasekaran and Gawankar, 2020; Kamilaris, Kartakoullis

and Prenafeta-Boldú, 2017; Wolfert et al., 2017), and from the literature, we reviewed so far, no

studies conducted on BDA In AISC in Ethiopia. Nevertheless, we found three articles which

mention - Big Data Analytics, Agriculture, and Ethiopia. The first two (McCarty et al., 2017; Neigh

et al., 2018) dealt with satellite area mapping study using Big Data - which is not in our scope. The

third one (Akal et al., 2019) is a Big Data case study on four organizations in Ethiopia - which is

not in our scope again. Moreover, it worth noting that though Akal et al. (2019) seems to set out

to research BDA, their research mostly dealt with structured data, and the paper has no mention of

unstructured data such as social media data. However, Akal et al. (2019) have listed several

challenges for the application of BDA in Ethiopia including data quality problem, lack of data

collection and handling standards, knowledge and awareness issues and lack of management focus.

To conclude this section, for BDA in AISC to achieve its intended goal, it is critical to understand

the topic from the participants perspective, and the topic in Ethiopia context remained unexplored.

A broader understanding of the expectations of participants, the requirement and implications of

BDA in AISC might shed light on practical challenges of the industry, and help to understand

practice and knowledge gaps more clearly.

1.2 Purpose Statement and Research Questions

1.2.1 Purpose Statement

The purpose of this qualitative study is to explore the potential application of BDA in AISC from

the perspectives of SCM professionals. The study aims to understand the expectations of

participants, the requirement and implications of BDA in AISC from the participants perspective.

At this stage in the research process BDA for AISC in Ethiopia defined as the application of

various data analytics techniques in to structured and unstructured data - in order to generate

insight and actionable knowledge to aid data-driven decision making for supply of input in

agriculture.

1.2.2 Research Questions

Based on the purpose of the study, this research asks the following questions from the perspectives

of the SCM professionals in the context:

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RQ1 - What are the expectations of the participants in the application of BDA in AISC?

RQ2 - What are the requirements for applying BDA in AISC?

RQ3 - What are the potential implications of applying BDA in AISC?

1.3 Topic Justification

This research might interest several public and private organizations. Considering the potential

implications of BDA in the agriculture supply chain, the study might provide useful insight to deal

with the current food security and sustainability challenges in the country. A better understanding

of the expectations of the participants might provide useful insight to motivate the adoption of the

technology. It may also generate useful inputs for policy development. In addition to societal

interest, the research may be used by technology and agribusiness companies to understand the

participant’s requirement - and this understanding may provide useful insight on how to develop

BDA enabled solutions tailored to the context.

Considering the unavailability of prior research in the context (see section 1.3) - the study might

provide valuable contribution to the scholarly research and literature in the field of study.

1.4 Scope and Limitations

The participants for this study were purposefully selected supply chain management professionals

whose work is related to agricultural input supply in Ethiopia. The participants work for fertilizer,

seed, crop protection and agribusiness organizations in the country. The study focused on the

perspectives of the participants in the study topic. The study addressed the challenges and

potentials in the topic area; however, the research did not address how to apply the technology in

the research setting. The documentary examination task only focused on relevant materials

published since 2015 - to help the researchers address the recent trends in the domain.

This research is subject to a number of limitations. BDA is a new phenomenon, particularly in

Ethiopia. Thus, the unavailability of prior research in the country is one of the limitations of this

research. The research focused on a new subject with an unknown element, and we chose to follow

a qualitative exploratory approach, and the data was collected through semi-structured interviews

and document analysis. Based on this the research has taken limitations which comes with the

selected data collection methods - for example, the presence of the researchers in the interview

process might have created biases on the responses of the participants (Creswell and Creswell,

2018).

The short time frame for the project was undoubtedly one of the limitations of this thesis; Thus,

the findings of the study is limited to the data which was collected and analyzed in the limited time

frame.

1.5 Thesis Organization

This thesis has six sections. And the organization of the thesis explained as follows:

Section 1 – This is the introduction part - Here we introduce the background, the research

setting, the topic area, purpose statement, the topic justification, the scope, limitations and

the organization of the thesis.

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Section 2 – In the Literature review part, we discuss the literature search process, the

method applied, the relevant concepts and theories from past studies in the topic. Moreover,

our theoretical approach will be discussed in this part.

Section 3 – In the methodology section, we discuss our approach and the research process

in detail, including ethical considerations.

Section 4 – In the empirical finding section, we present the data and findings from the

interview, documentary analysis.

Section 5 – In the discussion section, we present the analysis with the findings of primary

data, secondary data and literature review.

Section 6 – In conclusion, the contribution of the study to the theory, practical application

including impact on industry, policy and recommendation on further research will be

presented.

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2 Review of the Literature

In this section, we first explain how we conducted the literature review. Then we present the basic

concepts found from the literature review. Finally, the theoretical framework for the study will be

presented.

Abbreviations: RBV – Resource Base View, ASC – Agriculture Supply Chain, AISC- Agriculture

Input Supply Chain, SCM – Supply Chain Management, GIS – Geography Information System,

GPS - Global Positioning System, OLAP- Online analytical processing

2.1 Literature search Process

In this thesis, we follow Levy and Ellis (2006) systematic framework to conduct an effective

literature review. The objective is to analyze and synthesize high-quality peer-reviewed articles

and establish a strong foundation for the proposed topic and methodology; and, to justify that the

proposed study provides a novel contribution to scholarly research and literature in the field of

study (Levy and Ellis, 2006). Based on this the literature review is conducted in three stages: Inputs

- literature collecting and screening, processing based on bloom's taxonomy and output - writing

the final literature review - output (Levy and Ellis, 2006).

2.1.1 Inputs - Literature collection and screening process

In the literature collection and screening stage, a combination of 13 different keyword searches

were carried out in Scopus database. The search is limited to peer-reviewed articles published in

the last 10 years. The year limitation is based on the fact that the research topic is a phenomenon

in the last 10 years. Based on the search, a total of 61 articles were found, of which 5 articles

repeated several times (see appendix 1). With the inputs process going, 8 more articles are found

which contained much richer aspects from both horizontally and vertically in the researcher area.

For example, the BDA applications in AISC, different type of analysis which all will be

discussed in the following sections.

2.1.2 Processing - Based on bloom's taxonomy

In this part the literature process was conducted in accordance with Bloom's taxonomy which

include knowing and comprehending the literature, applying, analyzing, synthesizing and

evaluating (Levy & Ellis, 2006). The selected articles are initially filtered by the related

keywords, then after read the abstracts and main frame of the articles, more articles filtered. In

the middle of the filter process, more articles added in the list which are picked from the

reference list of corresponding articles. In order to conclude as much as whole picture of our

research area as well as make the gap above the sea.

2.1.3 Output - Writing the final literature review

The literature review mainly conducted from answering a few questions, such as: What are the

basic concepts of our research area? How do these concepts apply under different background?

What’s the connections between them? What are the challenges and gaps of BDA in AISC? And

how RBV connects with BDA and AISC? Then some barriers and challenges are proposed

regarding the research questions.

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2.2 Basic Concepts

2.2.1 Big Data

Modern digital technologies can better understand complex agricultural ecosystems and meet the

increasing challenges of agricultural production. These technologies can continuously monitor the

physical environment and generate large amounts of data at an unprecedented rate (Kamilaris,

Kartakoullis and Prenafeta-Boldú, 2017). These technologies generate large amounts of data,

called big data, for example, there is data from continuous measurement and monitoring of the

physical environment, sensors on fields and crops provide granular data points on soil conditions,

and detailed information on wind, fertilizer requirements, water availability and pests (Nidhi,

2020). These all belong to the scope of smart agriculture, smart agriculture helps to automate

agriculture, collect data from the field, and then analyze it, this can help farmers decide to plant

the right crop at the right time and achieve the purpose of making an informed decision (Nidhi,

2020). Big data in agriculture also has the dimensions of Volume, Velocity, Variety, Veracity,

Value, but big data is notorious for its accuracy and stability, so from such a large amount of data,

how to extracting information and doing the accurate prediction in a reasonable time is the key,

this behaviour is also known as big data analysis (Nidhi, 2020 ; Kamilaris, Kartakoullis and

Prenafeta-Boldú, 2017).

2.2.2 Big Data Analytics

Data volume is not the biggest issue in big data, the sources of data are mostly heterogeneous, the

volume and speed of data are also different (Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017).

They are expressed in different types and formats, and the access to data is also different (for

example, web services, Repositories, feeds, files, archives, etc.), these facts all point the issue in

one direction: the ability to search, aggregate, visualize and cross-reference large data sets in a

reasonable time. It is about the ability to extract information and insights, that is, the big data

analysis which also mentioned above(Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017). When

big data analytics is linked to agricultural SCM, new challenges arise.

2.2.3 Big Data in Supply Chain Management

Big data gradually becomes an important information technology regarding agricultural food

supply chain decisions (Ahearn, Armbruster and Young, 2016). Big data cannot be used separately

from society and ethical issues and also is actively constructed and explained by people, especially

in social and technological environments (Bronson and Knezevic, 2016). Especially today's supply

chain professionals are overwhelmed by massive amounts of data, which has inspired people to

think about new ways to generate, organize, and analyze data. This provides an impetus for

organizations to adopt and improve data analytics functions such as data science, predictive

analytics, and big data to enhance supply chain processes and ultimately improve performance

(Hazen et al., 2014). This is a very complex workflow that needs to be integrated across different

disciplines. For example, information systems experts to gain insight into how to collect, store,

process and retrieve data. SCM experts need to ensure that the analysis being performed is the

correct problem, and the results of the analysis are relevant, etc. (Hazen et al., 2014). This paper

is based on this purpose, hoping to link BDA and SCM in the Agri-food area.

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2.2.4 Big Data Analytics in Agriculture Supply Chain

First of all, it is certain that the analysis of these (big) data will enable farmers and enterprises to

extract value from it, thereby increasing their productivity (Kamilaris, Kartakoullis and Prenafeta-

Boldú, 2017). The scope of big data is not limited to agricultural production but also affects the

entire food supply chain, big data needs to be unlocked smartly, analysis has the potential to add

value at every step, it can start by choosing the right agricultural inputs, monitoring soil moisture,

tracking market prices, controlling irrigation, finding the right point of sale, and getting the right

price to processing value chain (Nidhi, 2020). Coincidentally, Wolfert et al., (2017) also mentioned

that the application of big data in smart agriculture goes beyond primary production, it is affecting

the entire food supply chain, and it is being used in many aspects, for example, providing predictive

insights into agricultural operations, driving real-time operational decisions, and redesigning

business processes for game-changing business models.

2.2.4.1 Social, environmental and economic aspects

Kamble, Gunasekaran and Gawankar (2020) mentioned that emerging technologies such as BDA

are pushing the traditional AISC towards a data-driven digital supply chain environment. In this

transformation process, we must consider not only food production methods, but also social

concerns, environmental concerns, food safety and quality requirements, and economic feasibility

(Kamble, Gunasekaran and Gawankar, 2020). Also, these solutions should not be limited to

agricultural production but should cover the entire supply chain, including food processing,

packaging, distribution and consumption (Kamble, Gunasekaran and Gawankar, 2020). This will

allow decision-makers to have the information they need to develop a sustainable supply chain

strategy (Kamble, Gunasekaran and Gawankar, 2020). When focusing on the environment, in

addition to the control of the natural environment, the political environment was also mentioned.

Issues such as insufficient land use, high rents, limited processing capacity, and hostile political

environments are major challenges in the development of alternative supply chains (Kamble,

Gunasekaran and Gawankar, 2020). According to Kamble, Gunasekaran and Gawankar (2020), a

high percentage of the literature are concerned about environmental issues, Sharma, Kamble and

Gunasekaran (2018) proposed that big data analysis plays a role in improving the quality of GIS

applications in agriculture, such as land selection, resource allocation, etc. But economic issues

are not ignored.

From the social perspective, Kamilaris, Kartakoullis and Prenafeta-Boldú (2017) mentioned from

a sociopolitical perspective that farmers may rely on the AISC because of the monopoly of the

agricultural food industry. Also starting from the business environment and the institutional

environment, the supply chain in the field of agricultural food is usually dominated by large

agricultural technology suppliers (participants who collect, aggregate and process data) (Lioutas

et. al., 2019). Lioutas et. al. (2019) also mentioned that social and environmental issues are often

ignored, such as climate change, soil erosion and irrigation, water shortages, and decreasing output

will challenge the agriculture supply chain, under the social environment, population growth and

management issues will also challenge the agriculture supply chain. Lioutas et. al. (2019)

expressed views from the perspective of the social environment, it is linked to food quality and

safety issues. However, facing those issues, the application of big data analysis in the agricultural

field can help farmers optimize their farm operations and its application in AISC is growing at a

faster rate (Kamble, Gunasekaran and Gawankar, 2020). BDA is used to analyze and predict

weather data, crop-related data, etc. (Kamble, Gunasekaran and Gawankar, 2020).

Ansari and Kant (2016) mentioned that facing the increasing globalization and marketization

challenges, integrating the concept of sustainability into the core business functions of the supply

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chain is an act that enables organizations to gain competitive market position, which is specifically

manifested in three fields' convergence, the three fields are environment, society and economy. At

the same time, from the perspective of RBV, gaining competitive market position means the

organization should have unique resources and capabilities, big data analysis is one of the best

resources/capabilities in this context (de Camargo et al., 2018).

The sustainability of the supply chain can be reflected in the management of physical things,

information, and capital flows, as well as cooperation between companies in the supply chain

(Ansari and Kant, 2016). For example, organizations will promote friendly strategies in different

fields at different levels of the supply chain (ibid.). In the environmental field, environmentally

friendly products and cleaner production methods are being promoted (ibid.); the practice of SCM

has also triggered changes in materials and energy efficiency (ibid.). This can not only improve

the economic efficiency of the organization but also bring brand effects to the organization in the

market and cause social impact (ibid.).

2.2.4.2 Big data Analytics applications in the Agriculture Supply Chain Process

Many big companies have announced the benefits of using big data analytic (Sanders, 2016).

Industrial companies, governments, and semi-official agencies (such as telecommunications,

education, agriculture, etc.) have recently expressed strong interest in the high value-added

potential of big data analytic (Addo-Tenkorang and Helo, 2016). The ability to capture store,

aggregate, and analyze data, as well as the ability to extract data into information, is quickly

becoming a task for almost all organizations (Sanders, 2016). Such as 7-11 Japan, the company

has a strategy that focuses on freshness, with an information system that collects information from

suppliers and distributors and they share all the data, the system supports just-in-time supply chains

(ibid.). By analyzing the sales trend of each item per hour, the company optimizes delivery plans

and reduces waste (ibid.). More specific examples are milk products; the company will start from

the customer's needs and reschedule several times a day (ibid.). There are small boxes of milk for

workers to take away in the morning, lunch for students at noon, and larger boxes at night for

parents to bring milk home at night (ibid.). Every aspect of this sort of time, quantity, display and

delivery is optimized based on data and coordination with suppliers (ibid.). This is undoubtedly

also related to our agricultural environment. Similarly, many types of agricultural products will be

added to the ranks of "milk". In fact, this also proves that the application of big data analysis in the

supply chain process can also help companies respond quickly to make correct decisions

(Arunachalam, Kumar and Kawalek, 2017).

When we put our focus on specific applications, almost all stages of supply chain management is

applying big data analysis:

1. Producing application. Applying big data to the analysis of the supply chain can enable

manufacturers to have an accurate understanding of their production capacity levels

and understand the costs of different products (Arunachalam, Kumar and Kawalek,

2017). Moreover, it can also help different products' manufacturers to adjust production

to ensure the resources assign and combine correctly (ibid.).

2. Marketing analysis applications, which are located at the sales end of the supply chain

(Sanders, 2016). These applications use big data analysis to focus on capturing

customer demand, micro-segmentation and predicting consumers' behavior (ibid.). For

example: In order for companies to achieve the goal of quickly adjusting their customer

strategy, companies can use technology to collect and track individual customer

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behavior data in real-time, and then combine this data with traditional market research

tools to gain greater insight force (Sanders, 2016). In addition, requirement planning is

equally important (Arunachalam, Kumar and Kawalek, 2017). The supply chain

manages processes and operations such as meeting customer needs which is one of the

key aspects of supply chain management ((ibid.). These references can also be used in

the agricultural environment. After all, the main difference between the two is the

application environment, and there are many similarities in other aspects.

3. Another application is logistics applications. Minimizing transportation costs is

something organizations can love, in the process of helping the products to be

transported through the supply chain, the goals of optimizing inventory, determining

the best distribution centre location and supply route can be achieved with the help of

big data analysis technologies, the big data technologies include but not limit big data

telematics and route optimization technologies that support GPS (Sanders, 2016). In

addition, big data analysis applications can also provide granular information and track

inventory status (ibid.).

4. Operations Applications: This stage mainly introduces the field of labour analysis in

operations, the application of big data technology can optimize labour, track

attendance, and reduce costs while ensuring service (Sanders, 2016). For example,

farmers can analyze the performance of farmworkers, such as the quantity of

agricultural products they processed every hour.

5. Sourcing Applications: Many companies report using analytics to optimize

procurement channel selection and integrate suppliers into their operations, these data

sources include expenses, supplier performance assessments, and internal or external

negotiations (Sanders, 2016; Arunachalam, Kumar and Kawalek, 2017). For example,

Amazon uses analytics to determine optimal purchasing strategies and manages all

logistics to get products from manufacturers to customers (Sanders, 2016). The analysis

is used to determine the correct combination of joint replenishment, coordinated

replenishment, and single purchase (ibid.).

Finally, in terms of these modules of the supply chain, the diversity of logistics and operation

applications have continued to grow, and procurement has lagged, but it will grow in the future

(Sanders, 2016). These examples show that the supply chain is a system, which also means that

functions need to be linked and coordinated to make the system work, optimizing one supply chain

function is not enough, the cost of optimizing one function usually leads to an increase in the cost

of another function (Sanders, 2016; Ansari and Kant, 2016). For example, marketing may

customize products by analyzing applications, however, if operations cannot produce the

corresponding products' versions and quantities, or if logistics do not have or cannot deliver them,

the system will perform poorly (Ansari and Kant, 2016). In other words, BDA has not only been

used to help companies make strategic decisions in procurement, supply chain network design,

and product design and development, but it should also be applied to all stages of the entire supply

chain management (Arunachalam, Kumar and Kawalek, 2017)

2.2.4.3 Analysis in Supply Chain Management:

In this section we discuss the three types of analytics: descriptive, predictive and prescriptive.

15

2.2.4.3.1 Descriptive analytics:

This is a data analysis used to describe past business situations to make trends, patterns and

anomalies visible; it tries to answer what happened by identifying problems and opportunities in

existing processes and functions (Ansari and Kant, 2016; Arunachalam, Kumar and Kawalek,

2017). It includes technologies such as Online Analytical Processing (OLAP) technology, standard

reporting (Ansari and Kant, 2016; Arunachalam, Kumar and Kawalek, 2017).

2.2.4.3.2 Predictive analytics:

Predictive analysis (PA) analyzes real-time and historical data, using mathematical algorithms and

programming to discover interpretation and prediction patterns within the data, in other words, it

makes predictions in the form of probabilities for future events (Ansari and Kant, 2016;

Arunachalam, Kumar and Kawalek, 2017). It is the purpose of this type of analysis to accurately

predict what will happen in the future and to provide the causes as much as possible (Arunachalam,

Kumar and Kawalek, 2017). Predictive analytics is achieved through the use of techniques such as

data/text/web mining and prediction, which are usually algorithm-based techniques such as

machine learning techniques (Ansari and Kant, 2016; Arunachalam, Kumar and Kawalek, 2017).

2.2.4.3.3 Prescriptive analytics

A prescriptive analysis is mainly to determine and evaluate a large number of complex goals and

alternative decisions. It mainly uses data and mathematical algorithms to achieve the purpose of

improving business performance (Arunachalam, Kumar and Kawalek, 2017). Prescriptive analysis

includes multi-criteria decision making, optimization, and simulation (ibid.).

Predictive and prescriptive analytics play a vital role in helping companies make effective

decisions about the strategic direction of the organization, and they support the value proposition

of the organization's business (Arunachalam, Kumar and Kawalek, 2017). They can be used to

deal with issues such as organizational culture, purchasing decisions, and supply chain

configuration (ibid.). Descriptive analysis answers questions about what happened and/or what is

happening. It is driving the supply chain, and it uses models, technologies and tools to help

companies make performance analysis that is fast, efficient and effective. ((ibid.)

2.2.5 Risks and Challenges of Big Data Analytics in Agriculture Supply Chain

2.2.5.1 Monopoly and Role Change

The effective management of supply chain resources can result in the enterprises becoming more

competitive, and this management requires a high degree of coordination between corporate

activities, information sharing capabilities, and stakeholders (Kamble, Gunasekaran and

Gawankar, 2020). From a resource-based perspective, sustainable competitive advantage is

achieved through the acquisition and control of supply chain resources (Kamble, Gunasekaran and

Gawankar, 2020). Agriculture is experiencing a digital revolution, some collection and analysis

tools for big data applications in the agricultural food sector may have an impact on the power

relationship between actors in the food system (such as farmers and large companies) (Bronson

and Knezevic, 2016). Coincidentally, Wolfert et al. (2017) also mentioned that in the existing

agrifood supply chain, the roles and power relations between different participants have undergone

major changes. They observed changes in the role of new and old software vendors in big data,

agriculture, and emerging data-driven initiatives (Wolfert et al., 2017). Therefore, the agricultural

16

product supply chain should aim at the BDA. As described in Kamilaris, Kartakoullis and

Prenafeta-Boldú (2017), the application of modern digital technologies and a better understanding

of complex agricultural ecosystems can cope with the increasing challenges of agricultural

production. The technology mentioned above can be the big data analysis, which will enable

farmers and companies can extract value from it and increase their productivity (Kamilaris,

Kartakoullis and Prenafeta-Boldú, 2017). The difference is that Lioutas et. al. (2019) regards big

data as a constantly changing tool generated by all participants in the agri-food supply chain, but

farmers have limited ability to deal with the complexity of the data, coupled with their role as

producers and the dual role of users hinders the institutionalization of big data, which echoes the

point mentioned in Bronson and Knezevic (2016) to a certain extent. Although all are participants

in the food system, the power relationship between peasants and large corporations is worth further

study.

In response to this problem, Ahearn, Armbruster and Young (2016) believes that big data is used

as a tool to organize and manage the supply chain of agricultural products. Big data in a centralized

global supply chain may lead to the loss of consumer choices this is a sign of market prosperity

(Ahearn, Armbruster and Young, 2016). Lioutas et. al. (2019) given this problem from the

perspective of collecting and analyzing data, they believe that the participants who engage in this

have obtained the largest share of value from big data, which is also the cause of the power

imbalance, that is, the inequality value obtained from big data. So Lioutas et. al. (2019) extends

the data to the concept of value, big companies in the field of smart agriculture seem to be the only

ones capable of processing and producing these data and obtaining their value. Today, there is no

doubt that the distribution of power on this issue follows the investments of different actors in

labour, technology, human resources and expertise (Lioutas et. al., 2019).

However, this immediately leads us to such consideration. Farmers seem to be one of the creators

of data or value, but large companies are participants in gaining value, so the discussion on the

ethical issues of farmers themselves and the data related to them are worthy of attention.

2.2.5.2 Ethics and Privacy

Big data cannot be used separately from society and ethical issues and also is actively constructed

and explained by people, especially in social and technological environments (Bronson and

Knezevic, 2016). Big data shows the relationship between agriculture food system participants,

farmers and enterprises, and Bronson and Knezevic (2016) advocate that it is time for scholars to

get involved in big data due to the infrastructure of big data has not been settled yet. The impact

on the environment, society and human beings was decided by the interaction between technology

and social ecology (Bronson and Knezevic, 2016). Specific to this aspect of the agriculture supply

chain, Wolfert et al. (2017) stated that the future of smart agriculture may either be closure or open

which means the farmers’ proprietary system either be highly integrated or separate of the good

supply chain. These developments raise questions about data ownership, data value, privacy, and

security which will further generate security and privacy issues, and also the stakeholder network

is decided by the big data solutions’ infrastructure (Wolfert et al., 2017).

In addition to the aspects described above, in the agriculture supply chain field, food safety is also

an issue that we should pay attention to. At the same time, the population keeps growing and is

happening in land degradation and water pollution (Ahearn, Armbruster and Young, 2016 ;

Kamilaris, Kartakoullis and Prenafeta-Boldú, 2017). In the face of these various problems, we

have reasons to believe that the application of BDA in the agriculture supply chain’s

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environmental, economic, social, sustainable, and stakeholder research will be a solution to the

problem.

2.2.5.3 Barriers

When technology and tools are applied, there are also ethical and privacy concerns. The specific

configuration and use of big data in the food and agricultural fields may have ethical impacts, in

this field, the following questions may arise, who has ownership of the generated data, who has

access to data produced by agricultural equipment, etc. (Bronson and Knezevic, 2016)

Some authors believe that due to the application of big data, the role and the power relations

between different participants in the current food supply chain network has undergone a significant

change (Wolfert et al., 2017). s include powerful technology companies, venture capitalists, and

often small startups and new entrants, who show an interesting game between them (ibid.). At the

same time, (Wolfert et al., 2017) mentioned that the future of smart agriculture might face two

extreme situations: 1) farmers have a highly integrated proprietary system of food supply chain or

farmers and other stakeholders contribute a flexible chain network, and 2) they can choose a

business partner and a collaboration system for food production technology (Wolfert et al., 2017).

At the same time, there are still some obstacles on the road to achieving sustainable supply chain

management, for example, lack of information transparency and lack of professional skills training

(Ansari and Kant, 2016). These are the result of the failure of supply chain management to be

successfully implemented, and they may be caused by costs, lack of commitment from top

management, lack of training and expertise, and poor supplier commitment (ibid.).

Data ownership and related privacy and security issues must be addressed appropriately. However,

if the application is too strict, it will also slow down innovation, at the same time, as the number

of data increases and real-time performance increases, data quality has become more challenging

(Wolfert et al., 2017).

2.3 Theoretical framework

Researchers who examined BDA widely used resource-based theory to conceptualize BDA as a

resource in achieving sustainable performance (de Camargo et al., 2018). For example, Gupta and

George (2016) draw on RBV to identify a list of tangibles, intangible and human resources to

develop BDA capabilities and improve organizational performance. Similarly, Erevelles et al.

(2015) build on RBV to discuss the moderating roles of physical, human and organizational

resources for big data collection, storage, extraction and application to improve dynamic

capabilities. In BDA and supply chain management literature, Gunasekaran et al. (2017) applied

RBV to conceptualize BDA adoption process and its impact on supply chain performance. As

discussed by Gunasekaran et al. (2017), information sharing and connectivity with management

commitment are critical resources for the assimilation of BDA and its impact on supply chain and

organization performance. In the same way, Yu et al. (2017) draw on RBV to investigate the

impacts of data-driven supply chain on financial supply chain capabilities. Furthermore, Wamba

and Aktor (2019) draw on RBV and dynamic- capability theory to develop big data-enabled

Supply Chain Analytics Capability Model. As discussed by Wamba and Aktor (2019),

management, technologies and talent in the supply chain are critical foundations for supply chain

analytics capabilities.

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2.3.3 BDA and RBV

Organizational resources and capabilities are the criteria for distinguishing corporate performance

from competitive advantage, this is the view and main component of the resource-based view

(RBV) (de Camargo et al., 2018). The value of resources can be defined as the organization can

create that competitors cannot achieve, and scarcity is that it cannot spread to large number of

enterprises (ibid.). As for the impossibility of imitation, which means that the resources are

extremely difficult to be copied (ibid.). The RBV has been used by information systems, and it is

widely used by big data scholars (ibid.). The logic of applying RBV to explore the application of

BDA is that from the perspective of RBV since competitive advantage is managed from valuable,

uncommon and difficult to be imitated resources, at the same time, the applications of big data

analysis will make the organization's system generates a large amount of information (Barbosa,

Vicente, Ladeira and Oliveira, 2018). Analyzing this information can obtain the answer to the

problem and predict the solution (ibid.). For example, using descriptive analytics in supply chain

management is a data analysis used to describe past business situations to make trends, patterns

and anomalies visible, it tries to answer what happened by identifying problems and opportunities

in existing processes and functions, these can be achieved by using big data analysis which

generates competitive advantage for the organization (Ansari and Kant, 2016; Arunachalam,

Kumar and Kawalek, 2017; Barbosa, Vicente, Ladeira and Oliveira, 2018). De Camargo et al.

(2018) Proposed insights from RBV and resources that may improve BDA capabilities and thereby

improve corporate performance. Specifically, consider data, technology, sufficient investment, and

sufficient time as tangible resources (IT foundation), management and big data technologies as

skills, and the company's innovative data culture, to implement big data analysis and pursue

competitive advantages, at the same time, to obtain the same capabilities, other competitors need

to pay considerable costs (de Camargo et al., 2018; Kwon, Lee and Shin, 2014).

Specifically consider the aspects included in big data analysis, in which the quality of information,

data management, diagnosis and value generation processes are very important, and when using

big data analysis in the actual operation process, such as setting the optimal price, finding product

quality problems, and identifying inventory levels or detecting loyalty, big data analysis has unique

capabilities, so it can enhance the value of the business (de Camargo et al., 2018; Akter et al.,

2016).

Big data analysis (BDA) has the potential to improve demand forecasting, communication, and

better management of supply chain resources (Barbosa, Vicente, Ladeira and Oliveira, 2018). At

this point, we can even study the impact of BDA on the supply chain from the perspective of RBV

(de Camargo et al., 2018).

2.3.4 BDA, SCM and RBV

The supply chain includes manufacturers, suppliers, transporters, warehouses, retailers and

customers and includes product development, marketing, operations, distribution, financial and

customer service functions (Barbosa, Vicente, Ladeira and Oliveira, 2018). Supply chain

management is a business process by which organizations can quickly plan, organize, manage, and

deliver new products or services by working with partners, while big data analytics can automate

important parts of the data chain (ibid.). For example, big data analysis can optimize labor analysis

during the supply chain operation phase, which includes tracking attendance and optimizing labor

(Sanders, 2016). A more specific example is that farmers can analyze the performance of

farmworkers, such as the amount of produce they process per hour (Sanders, 2016). RBV's

perception is that resources alone cannot provide competitive advantages, but big data analysis can

19

identify resources and achieve information connection, it has a positive impact on the supply chain

and then achieves a competitive advantage at the enterprise and supply chain level (de Camargo

et al., 2018; Barbosa, Vicente, Ladeira and Oliveira, 2018).

2.3.5 RBV in this study context

As stated in section 1, this thesis set out to understand the expectations of the participants, the

requirements and implication of BDA for AISC from the perspectives of participants. This

understanding is essential as it seeks to reveal the expectations and requirements of the participants

in terms of resources. Moreover, understanding of the implications of the topic might provide

insight on achieving competitive advantage in the setting. In RBV literature, organizations may

achieve a competitive advantage when they have organized access to valuable, rare, imperfectibly

imitable and non-substitutable (VRIN) resources (Barney, 1991). As summarized by Braganza et

al. (2017), these essential resources include financial, physical, human, organizational,

technological and also other critical intangible resources such as goodwill and reputation. The list

of resources mentioned above has the potential to help us understand the expectations of the

participant in terms of resource gains and resource requirements - and the potential implication of

BDA in ACS from the perspective of participants in the study setting. In light of these

considerations, this study takes RBV perspectives in assessing essential resource questions and

further implications. To conclude, in this thesis - the application of RBV as a theoretical lens is

justifiable considering the motivation of the research, the research problem, the research questions

and similar studies in the topic.

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3 Methodology

This section is structured as follows. We start our discussion with a general introduction on

methodological traditions. Then, we outline our research approach followed by our chosen data

collection and data analysis methods. Then, we outline our approach to validity and reliability.

And finally, we conclude our discussion with ethical issues - we may consider in the entire lifecycle

of this study.

Abbreviations: BDA – Big Data Analytics, AISC – Agricultural Input Supply Chain, ASC –

Agriculture Supply Chain, NGO – Non-Governmental Organization, SC – Supply Chain

----------------------------------------------------------------------------------------------------------------

Table 1 Methodology

Description Approach Research Approach Qualitative

Research Strategy Basic qualitative study

Research Paradigm Interpretive

Data Collection Method Individual interview and Document Analysis

Data Analysis Method Thematic Analysis

Validity and Reliability Triangulation, Participant validation, Researchers

reflexivity, Rich thick discerption, Peer review and

examinations

Ethical Considerations Informed Consent, Confidentiality, Anonymity, Respecting

norms, culture etc..

3.1 Methodological Tradition

In research methodology traditions, researchers generally categorize research tradition under

quantitative and qualitative (Myers, 2019; Merriam and Tisdell, 2015). Some researchers, for

example, Creswell and Creswell (2018) add mixed research method to qualitative and quantitative

category – which is characterized by a convergent nature. Broadly speaking quantitative researches

emphasize the application of numbers, statistical tools and theory testing whereas qualitative

researches emphasize on words, meanings and may involve theory generation (Myers, 2019;

Creswell and Creswell, 2018; Merriam and Tisdell, 2015).

Quantitative researches characterized by quantity questions (e.g., how many, how much, census

questions), rigid nature and deductive process. (Myers, 2019; Creswell and Creswell, 2018;

Merriam and Tisdell, 2015). Examples of quantitative researches may include laboratory

experiment and survey research (Myers, 2019; Creswell and Creswell, 2018; Merriam and Tisdell,

2015). On the other hand, Qualitative researches characterized by quality questions (e.g., essence,

nature, open-ended questions), emerging nature (Myers, 2019; Creswell and Creswell, 2018;

Merriam and Tisdell, 2015). The typology of qualitative researches presented by researchers in

different ways. For example, Myers (2019) presents eight types of qualitative researches: action

research, case study research, ethnography, grounded theory, semiotics, discourse analysis,

hermeneutics and narrative research. Creswell and Creswell (2018) on the other hand discusses

five qualitative research strategies: case study, ethnography, narrative analysis, phenomenology

and grounded theory; to this typology, Merriam and Tisdell (2015) add basic qualitative study and

21

discuss six conventional approaches for qualitative researches strategies. As stated by Merriam

and Tisdell (2015), many researchers state basic qualitative study just as qualitative research study

OR interpretive qualitative study - without mentioning its particular type (i.e. ethnography,

phenomenology, grounded theory, case study or narrative research).

In addition to the research strategy, the distinction between qualitative and quantitative researches

also relates to philosophical assumptions the researchers hold in the study (Creswell and Creswell

2018). Merriam and Tisdell (2015) emphasis the importance of philosophical position in

researches by explaining how it highlights the researcher’s standpoints to the nature of

knowledge (anthology) and the nature of reality (epistemology). Philosophical positions have been

discussed by different researchers in different ways using various terminologies. For example,

Creswell and Creswell (2018) discusses four subcategories of philosophical positions (world

views) – which are postpositivism, constructivist, transformative and pragmatism. Mayer (1997)

and Orlikowski and Baroudi (1991) on the other hand, summarize philosophical positions (in

research) under positivist, interpretive and critical categories. Merriam and Tisdell (2015)

summaries philosophical positions in to positivist / post-positivism, interpretive / constructivist,

critical and Postmodern / Poststructural. Here it worth noting that Merriam and Tisdell (2015)

summary contrast positivist with post-positivism and interpretive with constructivist – bridging the

Creswells summary to other researchers. In this section, we discuss Myers (1997) and Orlikowski

and Baroudi (1991) summary on the three categories of philosophical assumptions (positivist,

interpretive and critical) - to set out our philosophical position in the study.

The positivist standpoint assumes that reality is objective and out there independent of the

observer; and it focus on experimentation, survey and theory testing; and it seeks to predict, control

and generalize (Orlikowski and Baroudi 1991, Myers 1997; Merriam and Tisdell, 2015).

The critical standpoint assumes there exists multiple realities; and focus on cultural, social and

political contexts where a specific reality maybe in domination; and seeks to expose the existing

status quo and with a goal of emancipating the dominated (Orlikowski and Baroudi 1991, Myers

1997; Merriam and Tisdell, 2015).

The interpretive standpoint, on the other hand, assume the existence of multiple realities; and the

focus is on people and their perspectives; and it seeks to interpret, describe and understand a

phenomenon under study (Orlikowski and Baroudi 1991, Myers 1997, Walsham, 2006

; Merriam and Tisdell, 2015).

In this thesis, we seek to understand – BDA’s potential in achieving better AISC in Ethiopia from

the perspectives of our participants. And, we chose to conduct our study using a basic qualitative

study (Merriam and Tisdell, 2015) as a research strategy; and interpretive perspective is followed

as our philosophical assumption. Furthermore, we draw concepts from Walsham (2006) in

conducting this interpretive research. The rationale behind our chosen research strategy and

philosophical assumption argued in the following section.

3.2 Methodological approach

The argument in the introduction part emphasizes that BDA in AISC in Ethiopia is a new

phenomenon, which is previously unexplored. Several researchers suggest a qualitative

exploratory approach to be followed when investigating a new subject with unknown elements.

Accordingly, this research is conducted based on a qualitative exploratory approach to obtain

better insight into the topic.

22

As stated in the Methodological Tradition section, qualitative research strategies presented by

different researchers in various ways. And in this research, we follow Merriam and Tisdell (2015)

six-fold topology to argue for the intended research strategy.

As stated by Merriam and Tisdell (2015) Basic qualitative study is one of the most widely used

qualitative research strategies across disciplines, and it is interested on how people construct and

interpret their world and assign meaning to their experiences (Merriam and Tisdell, 2015).

Additionally, Merriam and Tisdell (2015) highlighted that the other five qualitative research

strategies also share the fundamental characteristics of Basic qualitative study with some

additional dimensions. For example, a grounded theory focuses on building theory in addition to

understanding a phenomenon under study; Ethnography, on the other hand, focuses on

understanding a culture of the society the participants live-in, in addition to their interaction with

the others; A narrative analysis focuses on story of people and seeks to analyze the stories in order

to understand their meaning as told; A phenomenological qualitative study focuses on

understanding the structure of a phenomenon and its essence; A qualitative case study engages on

researching specific bounded system in detail (ibid.).

In Ethiopia, AISC is everywhere, and different stakeholders across various organizations are

involved with the phenomena under study. As stated in the introduction section, participants of the

study work for different fertilizer, seed, crop protection and agribusiness organizations in the

country. And, it is essential to consult these participants to fulfil the aim of the study. Based on

this, the study could not be delimited by a specific context in terms of participants – as to help us

ensure a greater understanding of the phenomena under study. Therefore, a qualitative case study

could not be selected instead a basic qualitative study (Merriam and Tisdell, 2015) is followed as

a research strategy – as it helps to understand the different perspectives of participants across

boundaries. Based on this, the researchers purposefully selected a relevant participant and

conducted semi-structured interviews and document analysis to have a better understanding of the

phenomena under study.

The concepts mentioned in the introduction and literature review section emphasizes the need to

understand BDA in AISC from the perspective of the participants. This required us to apply the

researcher’s knowledge and understanding of the context - to draw meaning from multiple realities

in the phenomena. Therefore, the researchers holds the interpretive philosophical standpoint to

interpret, make sense and construct knowledge from the views of multiple participants and the

researcher's understanding of the context.

To conclude, this thesis uses a basic qualitative study as a research strategy– and the researchers

hold interpretive philosophical assumption in the study – the researchers use their understanding

of the context to draw meaning, make sense and construct knowledge from the views of

participants.

3.3 Data Collection Methods

In basic qualitative study, data may be collected through interview, observation, or documentary

analysis (Merriam and Tisdell, 2015). In this thesis, we have used: interview as a primary and

documents as a secondary mode of data collection.

3.3.1 Interviews – Primary data

23

Merriam and Tisdell (2015) listed three types of interviews based on structure: highly structured,

semi-structured and unstructured interviews. Highly structured interviews are standardized

interviews characterized with questions with predetermined order and wordings – which are

mostly designed to collect survey data such as census and marketing survey (Merriam and

Tisdell,2015). Conversely, unstructured interviews characterized with flexible/open-ended

questions with exploratory nature – which can be applied to explore a new complex topic where

the researcher knows little about (Merriam and Tisdell, 2015; Johannesson and Perjons, 2014).

And there is Semi-structured interview which use some pre-formulated questions – with some

level of consistency across different interviews – but with no strict adherence to the pre-formulated

questions – and with new questions emerging during the interview process (Myers, 2019). Semi-

structured interviews are ideal when a researcher require to collect specific data from all

respondents – and the interview process can generally be guided by an interview guide – with a

list of issues or questions to be explored – however all issues/ questions on the interview guide

applied flexibly with no predefined order or wording (Merriam and Tisdell,2015).

As argued in the introduction section, BDA in AISC in Ethiopia is a new phenomenon, which is

previously unexplored. And the present research set out to understand the expectations of

participants, the requirements and implications of BDA in AISC from the perspectives of all

participants. Based on this, we intend to conduct Semi-structured interview to help us create a

better understanding of the participant's perspectives into the phenomena under study. With Semi-

structured interview we aim to find answers to our research questions from the interviews with

different participants – with the aim of achieving flexibility as well as some level of consistency

and focus across different interviews – with participants encouraged to add important insights (as

they arise) to help us enrich the study with new lines of enquiry that might emerge in the interview

process (Myers, 2019). For this to be achieved we designed an interview guide (see Appendix 3)

based on our research questions. As suggested by several researchers, the interview guide include

a list of issues or open-ended questions - and applied flexibly with no predefined order or wording.

In terms of interview conducting process, Creswell and Creswell (2018) discusses four types of

interviews: face to face, telephone (internet), focus group and email interview. Due to the current

COVID 19 epidemic and consequent travel restrictions – in the research period, the interview

process for this study was not conducted face-to-face. Conversely, the researchers planned to

conduct one-on-one interview via the internet. The researchers believe that the one-on-one

interview may allow the participants to elaborate their thoughts and may help the researchers to

explore the topic more clearly.

3.3.2 The participants

In terms of sampling techniques, the researchers has used purposeful sampling technique to select

potential participants that best serve the researchers to address the problem and the research

question (Creswell and Creswell, 2018). One of the authors is a supply chain management

professional from Ethiopia, with more than ten years of experience – and the researchers has

capitalized the knowledge and the network of the researcher to purposefully select the relevant

participants for the study. Accordingly, SCM professionals whose work is related to agricultural

input and that have good knowledge of the study topics were selected to inform the study. The

selected potential participants work for fertilizer, seed, crop protection and agribusiness

organizations. These organizations come from private, NGO and government organization in the

country.

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Based on the fact that BDA is an emerging phenomenon, and its application is not established in

Ethiopia, the researchers would like to state that sampling methods other than purposeful sampling

are impractical in the context. However, snowball sampling method was also used in the process

as used by Carolan (2016).

Here the researchers explicitly state that the researchers only hold a researching role and are not

an informant in this study.

At the beginning, invitation - email messages with support letter (find attached) was sent to 18

(purposefully selected) SCM professionals whose work is related to agriculture input supply in

Ethiopia. And eleven of the invited expressed their willingness to participate. Additional six

participants obtain through snow ball sampling technique, where existing participants referred us

to three SCM professionals and three rare to find (highly relevant) professionals whose work is

related to agriculture supply of input in the study setting. And, this interview data draws upon data

collected from Semi-structured interview involving 17 SCM professionals whose work is related

to agriculture supply of input in Ethiopia (see table 2). The participants work for the major agri-

business firms, farmer cooperatives, governmental and non-governmental organizations whose

work is related to the agricultural supply of input in the country.

In regards to the number of participants our focus was to address the SCM professionals’

perspectives from the different sectors involved in agricultural input supply in Ethiopia – and we

continued purposefully selecting new participants until a point of saturation for new information -

and until the available data reliably allow us to interpret, make sense and construct knowledge

from the views of participants.

Table 2 The different participants interviewed for this study

No. Participant Role Interview length in minute Audio recorded

1 P1 Manager 42.34 Yes

2 P2 Manager 31.35 Yes

3 P3 Manager 21.21 Yes

4 P4 Manager 66.44 Yes

5 P5 Manager 36.27 No

6 P6 SC Officer 32.00 No

7 P7 Consultant 47.23 Yes

8 P8 Manager 44.26 Yes

9 P9 Manager 24.57 Yes

10 P10 Manager 47.29 Yes

11 P11 Consultant 50.39 Yes

12 P12 Consultant 53.28 Yes

13 P13 Manager 35.49 Yes

14 P14 SC Officer 36.14 Yes

15 P15 Manager 41.30 Yes

16 P16 SC Officer 39.16 Yes

17 P17 Manager 23.54 Yes

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3.3.3 The Interview session

After confirming their willingness to participate, another email message was sent to each of the

participants with information sheet (see Appendix 1) and consent form (see Appendix 2) attached.

After agreement concerning suitable date and time, the Interviews occurred between April to May

2020. The length of the interviews varies between 21 minutes to 66 minutes.

Seven of the seventeen interviews were conducted via a regular telephone line. The other ten were

conducted via Telegram, WhatsApp and Skype services. To aid the data collection activity, the

researchers utilize note-taking (to document reflective and descriptive information) and audio

recording - in consultation with the study participants. All the participants were asked for their

permission to audio record the conversation, and all were willing except two participants. And all

interviews were audio-recorded except the two. The interviews were conducted in Amharic

language (official language in Ethiopia). One of the researchers is a native Amharic speaker, and

all recorded interviews were later transcribed to Amharic language – Ethiopian indigenous written

alphabet.

Each interview started by confirming with the participants that their informed consent – and that

they have received the information sheet and the consent form. And then the participants were

asked for their permission to audio record the conversation. Before raising the main topic, a brief

presentation about the study was provided. We invite the interviewee if there is any question they

want to ask before starting the discussion. The conversation in the semi-structured interview

focused on the perspectives of the participants in the study topic. As discussed above, each semi-

structured interview was aided by the interview guide (see Appendix 3) – in order to achieve some

level of consistency and flexibility across different interview sessions. (Myers, 2019). Using the

list of semi-structured questions as a guide, we have attempted to adjust in to the context in the

interview process, as some participants may have a lot to say into a specific area. As observed

during each session we posed the open-ended questions and then we actively listen to the

interviewee’s perspective and generated relevant questions based on the context when needed.

Before concluding the interview process - We invite the interviewee if they have any question in

regards to the study and the interview process. Finally, we thanked the interviewee for their time

and participation.

3.3.4 Documents – Secondary data

In addition to the interview, a documentary analysis is utilized as a secondary source of data. In

general, there are six types of documents which can be used in any research setting: public

documents, personal records, popular culture documents, visual documents, artefacts and

researcher generated documents (Merriam and Tisdell,2015). In this study, the researchers have

used authentic public documents as a secondary data source to inform the study. These documents

are published by the Agricultural Transformation Agency of Ethiopia, the Ministry of agriculture

of Ethiopia, Haramaya University and the U.S. government global hunger and food security

initiative – which are the key actors in agriculture in Ethiopia.

3.4 Data Analysis Method

The goal of the data analysis process is to reach to a finding (themes) that can best answer the

research questions (Merriam and Tisdell, 2015), achieve the study purpose and establish the

significance of the study. The data analysis task was conducted in parallel with the data collection

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activity (simultaneous procedure) – and it helped us to manage the data analysis process more

efficiently (Creswell and Creswell, 2018; Merriam and Tisdell, 2015). Furthermore, the theoretical

framework (see section 2.3.5) is used as our lens to investigate the data (and determine what

question to ask) in the complete data analysis process (Merriam and Tisdell, 2015).

In addition to the above-mentioned simultaneous procedure, we followed Braun and Clarke's

(2006) and applied the six phases of thematic analysis as data analysis procedure. Braun and

Clarke's (2006) six phases include:

1. Familiarizing with the transcribed data – In this phase we immersed ourselves in the data

and became familiar with the complete scope of the content. As recommended by Braun

and Clarke's (2006) - we transcribed the audio recording (and we generated 51 pages of

transcribed document from 12 hour of audio recording), and then we read, re-read, re-

read the transcribed interviews again - and we note down our ideas and reflection on the

paper. At this stage in the analysis process we used the research questions and our

theoretical framework (see section 2.3.5) to identify emerging patterns in the data. In this

phase we have also attempt to make a general sense and reflect on the thoughts of the

participants stated in the interview. Here we also took note of our impression concerning

the relevance and the reliability of the information gathered.

2. Generation code that describe the data – In this phase our initial codes were generated

and data which are relevant to each code were gathered from each of the transcribed

interviews.

3. Identifying themes in the codes – In this phase the generated codes were sorted and

gathered under potential themes that interest our thesis.

4. Reviewing the themes – In this phase we generated a thematic map by filtering list of

themes. At this stage few themes were deleted and some themes were combined.

5. Define, refine and naming of the themes – In this phase each theme were refined one by

one. Each theme were clearly defined and the data in the theme was analyzed – and in the

process some themes were renamed with more ideal names.

6. Generating the report – This phase is the final stage of the analysis. And here we

connected the final analysis to the research question and the literature review. We

attempted to present rich discussion and write-up on the themes, subthemes and the

participant's perspectives in the study topic.

No data analysis software is used in this research; rather, we make good use of MS-Word

(Lichtman, 2013) and MS- excel to help the data analysis, sorting and table creation process.

3.5 Anticipated Risks

This study is conducted at the time of COVID 19 epidemic and while a state of emergency declared

in Ethiopia due to the epidemic. Due to this reason, it was anticipated that – it may not be the right

time for some invited potential participants to participate in this study. And as anticipated, some

invited potential participants were not able to take part in the study, due to the current situation.

And this is mitigated by contacting and inviting more (other) participants through our existing

27

professional network. Snow ball sampling technique was also used, where existing participants

referred us to new relevant participants.

Time was another anticipated risk factor in the interview. Though, the time needed to participate

was always discussed in the consent process – it was anticipated that some interviews may take

more time, due to semi-structure nature of the interview. And as anticipated some interviews took

us more than the allotted time, for example the interview with participant 4 which was 66 minutes.

And this challenge is mitigated by avoiding possible overlapping plans, by allocating wider time ,

by planning anticipated participation time as time range.

3.6 Reliability and Validity

The present study is designed making sure the research approach is consistent with previous

researches in the area - ensuring "qualitative reliability" (Creswell and Creswell, 2018). Similarly,

the researchers followed clear guidelines to verify the accuracy of the study results – ensuring

"qualitative validity" (Creswell and Creswell, 2018).

In general, the following procedures are employed for internal validity:

- Triangulation: Primary data (one-on-one interview) and secondary data (document analysis) is used to achieve triangulation (Creswell and Creswell, 2018; Merriam and

Tisdell, 2015).

- Participant Validation: By sharing the analysis report to the participants - to make sure their perspective is represented accurately (Creswell and Creswell, 2018; Merriam and

Tisdell, 2015).

- Peer review & Examination: The entire research process is supervised and debriefed by our supervisor (Assistant Professor, Niclas Eberhagen ) – and this was a crucial process

to ensure the quality and accuracy of the research. Moreover, an ongoing peer-review

with other fellow students was used to enhance validity (Creswell and Creswell, 2018;

Merriam and Tisdell, 2015).

- Researcher's Position / Reflexivity: from the outset of this study - The researchers stated a possible bias; they may bring to the study (Creswell and Creswell, 2018; Merriam and

Tisdell, 2015). As we know it, this is a qualitative study and the researcher's background,

experience – interpretation is key to the process and undoubtedly shape the final resu