protocol development and verification of case...

Protocol development and verification of case studies 1

This project has received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreement N° 773297.

Protocol development and verification of case studies Date of document – July 2019 (M14)

D8.1: Report on verification and confirmation of case studies and the protocol toolkit.

Authors: Gianmaria Tassinari (UCSC); Stefano Boccaletti (UCSC); Claudio Soregaroli (UCSC) Monitoring the Bioeconomy

Ref. Ares(2020)1428666 - 08/03/2020



Technical References

1 PU = Public PP = Restricted to other programme participants (including the Commission Services) RE = Restricted to a group specified by the consortium (including the Commission Services) CO = Confidential, only for members of the consortium (including the Commission Services)

Document history

V Date Beneficiary Author

1 29.05.2019 Intern Gianmaria Tassinari et al.



Project Acronym BIOMONITOR

Project Title Monitoring the Bioeconomy

Project Coordinator

Justus Wesseler

Wageningen University

[email protected]

Project Duration June 2018 – May 2022 (48 months)

Deliverable No. D8.1

Dissemination level 1 PU

Work Package WP 8 – Case Studies

Task T 8.1 - Protocol development and verification of case studies

Lead beneficiary P2 (UCSC)

Contributing beneficiary(ies)

P14 (DTT), (P15) EFI, P4 (LLU), P17 (NOVA), P18 (STT), P5 (SUA), P12 (TI), P6 (TUM), P13 (WEcR)

Due date of deliverable

31 May 2019

Actual submission date

12 July 2019

mailto:[email protected]



1 Summary

WP8 deals with case studies carried out in the BioMonitor project and requires a protocol to guide the consortium partners to conduct them consistently. The protocol is a set of theoretical and practical guidelines that, according to the best practices in the literature, ensures all the relevant aspects in conducting a case study. The implementation of the protocol allows a consistent interaction across partners, increases the knowledge of the construction and the validity of a case study, and guarantees a significant contribution of WP8 to the BioMonitor project. Furthermore, the protocol facilitates better interaction with other WPs. The protocol outlines three main phases characterizing the conduct of a case study before, during and after data collection and analysis. The first phase concerns the design of case studies. It includes a detailed description of the proposed research questions and projects. This phase aims at defining and sharing among the partners what the objectives of the cases are and how they are achieved. The protocol provides guidelines for setting up the case study, anticipating key issues, attributes, evidence sources, tools and data collection methods. The second phase provides common rules on context analysis, data collection and analysis, facilitating both internal and cross-case analysis. The guidelines ensure consistency and interactivity in the data collection and analysis, establishing a chain of evidence between the collected data and the results. The third step defines a common framework for case study reports providing a detailed systematic and common structure of the report.



Table of content

0 SUMMARY 3

1 OVERVIEW 5

1.1 OBJECTIVE OF WP8 5

1.2 CONTACT INFORMATION 6

1.3 ROLE OF THE PROTOCOL 6

1.4 LAYOUT OF THE PROTOCOL 7

2 PHASE ONE: CASE STUDY DESIGN 9

2.1 GETTING STARTED 9

2.1.1 RESEARCH QUESTION(S) 9

2.1.2 APPROACHES TO THE COMMON RESEARCH QUESTIONS 15

2.2 SETTING CASES 17

2.2.1 OVERALL FRAMEWORK 17

2.2.2 SELECTING CASES AND UNITS 18

2.3 CRAFTING INSTRUMENT 19

2.3.1 IDENTIFY AND SELECT STAKEHOLDERS 23

2.4 LINKAGES WITH INDICATORS IN BIOMONITOR 24

3 PHASE TWO: DATA COLLECTION AND ANALYSIS 25

3.1 CONTEXT ANALYSIS 25

3.2 DATA COLLECTION 25

3.2.1 INTERVIEW 26

3.3 ANALYSING THE DATA 27

3.3.1 WHAT IS A CODE? 27

3.3.2 THE MECHANICS OF CODING 28

4 PHASE THREE: REPORTING 30

4.1 CASE STUDY REPORT 30

5 CONCLUSIONS 32

REFERENCES 33

APPENDIX 35

APPENDIX A 35

APPENDIX B 36

APPENDIX C 37

GLOSSARY 53



2 1Overview

The specific objective of this deliverable is to develop a case study protocol, verify and confirm the pre-selected case studies.

1.1 Objective of WP8

The objective of work package 8 (WP8) is to test, validate, and fine-tune the operationalization of the three pillars of the BioMonitor project and to provide feedback on possible improvements and recommendations for future implementation. WP8 aims at reaching its objective through case studies. Sector-specific and cross-sectoral cases have been pre-selected to support the replicability and the external validity of the research design. Sector-specific cases include new wood-products from forestry, biorefineries, chemical industry, and biobased additives in the feed and packaging industries. Cross-sectoral cases include applications of new data collection methods in Italy, Spain, Slovakia, and Latvia, as well as the Bioeconomy Pilot from the Vanguard Initiative. The expected outputs of these case studies include:

Lessons learnt and best practices for data collection;

Testing and validation of new bioeconomy indicators;

Provision of data for models applied in WP5;

Validation of outcomes from BioMonitor Model Toolbox.

Table 1 summarizes the pre-selected cases according to the expected outputs. All the case studies relate to validation of indicators and data provision defined by work packages 1-3. New wood-based products, chemical industry, packaging industry, and cross-sectorial cases will also provide new data for the models to be improved, extended, and/or newly developed as part of the BioMonitor Model Toolbox. Finally, all cases, except for the new wood-based products, will validate the BioMonitor Model Toolbox output at a cross-regional, cross-sector, and product levels. Partners participating in this task will follow a common and co-built protocol.

Table 1 – Case Studies in the BioMonitor Project and Outputs

Sector-specific cases Cross-sectorial cases

Output

Ne

w

wo

od

-

bas

ed

p

rod

uct

s

Bio

-

refi

ne

rie

s

Ch

em

ical

in

du

stry

Ad

dit

ive

s

in t

he

Fe

ed

Ind

ust

ry

Pac

kagi

ng

ind

ust

ry

Ital

y an

d

Spai

n,

Latv

ia a

nd

Slo

vaki

a

Van

guar

d

Init

iati

ve

Validation of indicators and data collection X X X X X X X

Providing data for models in BioMonitor Model Toolbox

X X X X X X X

Validation of outcomes of X X X X X X



BioMonitor Model Toolbox

1.2 Contact information

Table 2 lists the contact person for the case studies.

Table 2 – Contact persons

Case Study Institution Contact person

Sect

or-

spec

ific

cas

es

8.2.1 - New wood-based products EFI Mariana Hassegawa [email protected]

8.2.2 - Bio-refineries TUM Maria Vrachioli [email protected]

8.2.3 - Chemical industry NOVA Ángel Puente

[email protected]

8.2.4 - Dynamics of Bio-based Additives in the Feed Industry

TI Martin Banse [email protected]

8.2.5 – Circularity in the bio-based packaging industry

NOVA Elke Breitmayer

[email protected]

Cro

ss-

sect

ori

al

case

s

8.2.6 - Italy and Spain, Latvia and Slovakia

WU Dusan Drabik [email protected]

8.2.7 - Vanguard Initiative UCSC Gianmaria Tassinari [email protected]

1.3 Role of the protocol

The protocol is a set of guidelines that the consortium WP8 partners use to ensure the highest possible uniformity in data collection and analysis and to maintain consistent interaction with other WPs. The protocol facilitates interaction highlighting the specific role of each case, its setting, its analysis, the different types of expected outputs, and provides a common terminology. This allows the partners to have an overview of what the case covers and how, revealing possible synergies and divergences with other cases. In addition to being co-built by partners, the protocol is regularly updated and reviewed. To facilitate updating, the protocol is structured to include descriptive and summary tables. Partners will try their best to apply a common methodology according to the different outputs. In this way, the protocol ensures that (i) missions and goals of each case study reflect the interests of WP8, (ii) the comparative analysis of individual case studies is facilitated (Task 8.2); and (iii) feedback on possible improvements and recommendations for end users is significant and consistent (Task 8.3).










1.4 Layout of the protocol

Despite the frequent use of case study research, a common formal and explicit methodological path has not yet emerged (Yin, 2014). The literature suggests several procedures to be followed when conducting a case study with significantly different points of view and conceptualizations. A review of the literature, however, identifies a general agreement about individual tasks in a case study, although the details may vary (Brereton et al., 2008). We constructed our own protocol based on three reference works used in case study research: “Building Theories from Case Study Research” by Eisenhardt (1989), “The Art of Case Study Research” (Stake, 1995) and “Case Study Research Design and Methods” by Yin (2014). On completion of the layout of the protocol (Table 3), a do-list was included (Appendix A) and a process of validation was carried out using the checklist items developed by Runeson and Höst (2009) (Appendix B).

Table 3 – Layout of the protocol

Section Sub-section Purpose

Overview

Objective of WP8

Provides a brief overview of WP8's objectives and the motivation for a protocol template.

Contact Information

Role of the protocol

Layout of the protocol

Phase one: Case study design

Getting started

Research questions

Share research questions among partners and provide better grounding of measure. Facilitate to build and improve internal validity, raise the theoretical level and sharpen construct definition (Eisenhardt, 1989).

Approaches to the common research questions

Setting cases

Overall framework

Provide a clear rationale for the case study selection (Yin, 2014), sharpen external validity (Eisenhardt, 1989), anticipate key problems, events, attributes, spaces, persons (Stake, 1995), and facilitate the comparative analysis of the individual case studies (foreseen in Task 8.3).

Selecting cases and units

Crafting instruments Identify and select stakeholders

Facilitates the triangulation by multiple data collection sources improving construct validity (Yin, 2014), fosters divergent perspectives (Eisenhardt, 1989) and facilitates the data collection process and its uniformity (Maimbo and Pervan, 2005).

Linkages with indicators Verifies linkages with indicators, monitoring and modelling activities in BioMonitor.



Phase two: Data collection and analysis

Context analysis Provides ample and uniform details on the case study context sharpening external validity.

Data collection Interviews

Ensures uniformity in data collection and analysis, facilitating both within and cross-case analyses, especially for common data sources as interviews.

Analysing data What is a code?

Guidelines for data analysis recommending a chain of evidence (construct validity) through the use of the coding process.

The mechanism of coding

Phase three: Reporting

Case study report Case study report reviewed by partners to improve construct validity and transparency (reliability).

Appendix Provides common tools.

Glossary Makes participants familiar with the terminology.



3 Phase one: Case study design

This section provides a detailed description of the research questions and proposed designs to be shared among partners, the objectives of the cases are and how they can be achieved. Sharing information is a key driver for the validity, reliability and transparency of our scientific research. Therefore, it is a priority of the protocol to facilitate an information flow on the most relevant aspects of each case study. In addition, all partners will be encouraged to use common concepts of case study research, use the same terminology (see the Glossary), and move in the same methodological direction, while maintaining the required flexibility due to the heterogeneity of the case studies in WP8.

3.1 Getting started

To-do list:

Define research question(s) and objective(s);

Identify previous case studies and review the literature on the topic;

Define common WP8 research question(s);

Share and discuss within WP8 and WP leaders the research question(s).

Checklist items:

Are the objectives, preliminary research questions, hypotheses (if any) defined in advance clear?

Is the theoretical basis—relation to existing literature or other cases—defined?

Are the authors’ objectives made clear? Getting started by defining the research question and the case setting is the first activity suggested by the most relevant case study methodologies (Eisenhardt, 1989; Stake, 1995; Yin, 2014). We developed and shared a document (“Subtask 8.1.2: Verification of case studies”, Appendix C) to collect the main research questions and settings within WP8. The collected information is shared by means of this deliverable in the following sections thus ensuring a comparative analysis and discussion. The deliverable updates contain the necessary changes that have emerged during Task 8.2.

3.1.1 Research question(s)

The definition of a research question is one of the most important tasks, and it determines the nature of a case study to a large extent (case under study, units of analysis, data collection, analysis, inferences and generalization, validity) (Yin, 2014). An initial definition of the research question is supposed to have a well-defined focus, to provide a better grounding of designing and to collect specific kinds of data systematically (Eisenhardt, 1989). It is equally important to recognize that, although early identification of the research question and possible designs are



helpful, they may change during the research (Eisenhardt, 1989). The definition of the research question, data to be collected, and conclusions to be drawn are part of a continuous dynamic logic flow that makes the design of a case study in constant progress (Figure 1).

Figure 1 Research design

It is important to highlight from the beginning the convergence and difference across the questions of each WP8’s case study thus facilitating synergies and avoiding overlaps. We asked all partners to cooperate on the definition of their research questions. Table 4 shows the research questions according to the different outputs expected for each case study. The table will be updated to include the improvements that will emerge during Task 8.2.



Table 4 –– Case Studies’ Research Question(s)

Case Study Lessons learnt and best practices for data

collection Test and validation of new bioeconomy

indicators Providjng data for models in BioMonitor

Model Toolbox Validation of outcomes of

BioMonitor Model Toolbox

8.2.1 - New wood-based products

What are the main textiles, chemicals, plastics and wood fibre composites that could be economically produced from lignocellulosic biomass from forests in near to medium future?

What fossil-based chemicals or materials they could substitute?

What requirements are there for biomass quality and quantity?

To which extent are these products compatible with existing value chains?

8.2.2 - Bio-refineries

How do we identify bio-refineries?

What determines the bio-refinery location?

How is the bio-refinery’s supply chain designed?

How do bio-refineries shift a region’s employment rate?

Average productivity and efficiency estimation for high bio-refinery density regions.

How does the establishment of bio-refineries influence the land use change in a region?

How does bio-refineries shift a region’s share of renewable resources consumption?

How are local prosperity, reduction of poverty and promotion of human rights affected by employment related to bio-refineries?

At which level of the bio-refineries chain are the most of employment opportunities?

Providing data on employment of biorefinery sector as input for the BioMonitor models (MAGNET).

Are the estimated bioeconomy employment rate equal to the observed employment growth?



8.2.3 - Chemical industry

The chemical industry uses both fossil and bio-based resources. In WP3, different methodologies are developed to better quantify the share of the bio-based part of the chemical industry in economic, environmental and social indicators. This case study should be able to provide lessons learnt and guidance to best practices for quantifying these indicators.

An important driver towards bio-based chemicals is their environmental performance in terms of resource consumption, health effects, ecosystem’s quality and climate change. The case study should therefore, in particular, be able to quantify these environmental indicators for the selected part of the chemical industry. It will be tested and validated in this case study whether these and other indicators (e.g. market related) can be quantified for the selected subsector of the chemical industry. Are the quantified indicators in line with expectations of experts from the selected chemical subsector?

Can the indicators for the selected part of the bio-based chemical sector be quantified in such a way that they can meaningfully be used by WP5?

Are the modelled current and future market volumes of bio-based chemical products in line with expectations? What are the major limitations of the model? Can uncertainty be assessed?


Additive Industry uses both fossil and biobased materials, depending on materials and requirements of different uses. Quantifying the bio-based shares and their influencing factors compared to the fossil-based is an objective as well as its impact on economic, environmental and social indicator

Can the necessary data be collected to quantify biobased shares?

What are the factors?

Who are the relevant stakeholders in the value chain?

What are the driving forces for the market of feed additives as a whole and bio-based additives in particular?

Which elements are necessary for transparent and open communication with stakeholders to facilitate feed additives?

What are the potential to upscale the results of this study to a sectoral and European level?

It will be tested and validated whether in project defined indicators can be quantified for relevant sub-sector.

Are the quantified indicators in line with expectations of experts? Are they understand in the same way they have to be?

It will be tested whether the indicators will move in desired directions

Contribution to reduced import dependency on feed crops especially soybeans

Increase in resource efficiency of intensive animal husbandry

Reduction in nutrient leakages especially nitrate

Will provide data as input for the BioMat model

Has the potential to rise the performance of livestock sector

Has the potential to reduce leakage

Potential to reduce imports of feed crops and increased utilization of domestically grown fodder or an improved utilization of residues in animal feed.

Feed additives and the efficiency of feed use (difference bio- and fossil-based), are their differences in use

Can feed additives contribute to the reduction of imports of feed crops, especially soybeans/soy meal?

Do feed additives promote the use of domestically grown fodder?

Do feed additive reduce nutrient leakages (especially nitrate) from animal husbandry? And under which conditions?

Are the modelled current and future market volumes of bio-based additives in line with expectations? Are modelled impacts in-line with expectations (But no validation of model itself as the generated data serves as input the model)




Can sufficient data be collected to calculate the proposed circularity indicator developed in Task 3.7?

Can the procedure for data collection be generalised from the bio-based packaging industry to other bio-based sectors?

Does the circularity indicator developed in Task 3.7 adequately depict the circularity of different bio-based packaging materials?

Can the methodology for calculating the circularity indicator be equally applied for bio-based, partially bio-based and non-bio-based materials (interesting, because several important packaging materials, like TetraPak®, are only partially bio-based).

Does the circularity indicator capture all possible use and end-of-life options of the selected packaging materials (such as mechanical recycling, composting, incineration and in-situ biodegradation)?

Can the case study provide the proper data to the models in WP5 in order to improve the ex-ante analysis capacity of models for the circularity indicator compared to what is possible in current status of the models?

Is the modelled circularity indicator in line with expectations?


Can sufficient data be collected to cover the scope of the bioeconomy defined in the project?

Which bioeconomy indicators will be able to calculate in the four pilot countries?

Are the proposed bioeconomy indicators relevant to stakeholders?

Are these indicators already monitored?

Which data are needed to calculate these indicators and are the data available?

In order to improve the ex-ante analysis capacity of models for bioeconomy indicators compared to what is possible in current status of the models?

To which extent can the MFM feed the new BioMat model with more/better detail on bio-based materials flows (on supply and use side) for the four pilot countries?

Where do stakeholders agree with model outcomes for the four pilot countries and where not? What do they expect?

Can the models be improved (for example which relevant variables could be additionally included)?



8.2.7 - Vanguard Initiative

The overall objective is to study the dynamics and internal structure of an emerging bio-based supply chain.

What is the internal structure of an emerging biobased supply chain? What entities are involved in its network?

How are entities within this social context linked? How influential are they?

What are the driving factors that engage these entities to participate in the network? What are their goals?

To what extent are entities susceptible to changes? What is the resilience of the network?

Particular attention will be paid to the quantification of biomass, knowledge and money flows between network actors. We will therefore provide important feedback and recommendation on that data collection methods.

Is the set of proposed indicators likely to clarify the consensus on the objectives of the system/organisation?

Does this indicator really measure the issue?

Is it actually possible to calculate these indicators with meaningful data?

If the indicator is high or low, what does it actually tell you, and does it give enough accurate and precise information for you to be able to investigate further and take any necessary action?

Can the implications of the indicator results be communicated to, and believed/appreciated by the right audience?

To which extent can the case study provide proper data to improve the current state of the models, in particular with regard to bio-based materials and biomass flows?

Are the expected results of the model for the emerging sectors of the bioeconomy in line with the data that emerged from the case study?



3.1.2 Approaches to the common research

questions

As mentioned before, WP8 is itself a multiple case study and, therefore, there is also a need for common approaches to research questions. The common and general case study questions will reflect mainly the aims of WP8. Common approaches are decided, shared, and discussed among partners during Task 8.2. How should validation of outcomes of BioMonitor Model Toolbox be performed?

The validation of the BioMonitor toolbox output will be done triangulating the toolbox output with the information available from the local context of the case studies (using secondary data sources and expert interviews). The objective of this validation will be a qualitative assessment of whether the model outputs are rational and coherent in the local context.

How should validation of data collection and indicators be performed?

Validation of indicators and data collection should be performed using the following logic: 1) Use the data collection methodology from WP3; 2) Perform interviews/surveys with relevant stakeholders; 3) Data validation. Data validation are both data-driven (by double checking estimates from official statistics with data collected from surveys and industry representatives) and expert-driven (if the quantitative approach is not feasible, just perform a qualitative check of the feasibility of data collection methods from expert interviews). Given this logic, WP8 partners designed the following flowchart. Validation, indicating the plan for the validation process, tasks and deadlines.



Figure 2 Validation

WP3 deliverables and a short overview of the data collection methods, which illustrates for each method what it can do and what it cannot, represent the starting documents for the data collection validation process. These documents provide guidance for the short-, medium- and long-term methodologies that are developed to measure the EU bioeconomy and fill data gaps. Depending on the case study and respective indicators (as reported in section 3.4 - Linkages with indicators in BioMonitor), each partner will report on the methodologies chosen for data collection by January 2020. Subsequently, the involvement of stakeholders in the validation process is necessary. For these reasons, by the end of 2019, each case study leader will provide a complete list of stakeholders (name of the organisation, company or person) involved in their case study. Each case study has to start engaging stakeholders’ by January 2020. In the following period, interviews and surveys will be conducted to collect data and validate the same data collection. In support of this phase, a template will be shared with the key points and questions to be addressed. The template will be developed by a working group composed of: WEcR, as representative of WP5 and for the model's data needs; NOVA, WP3 representative, for the data collection methodologies chosen and developed in BioMonitor; TUM, leader of task 8.2; UCSC, leader of WP8. Through the data collection and the interviews and surveys conducted, it will be possible to validate both in qualitative (through the detailed description of the application of the data collection methodology, integrated with the answers to the questions from the interviews) and quantitative (through the data collected) terms. No extensions to the agreed-upon deadlines will be allowed.



3.2 Setting cases

To-do list:

Describe the overall framework of the case study;

Define the case under investigation and the chosen unit(s) of analysis;

Share and discuss within WP8 and WP leaders the adopted case study setting;

Checklist items:

What is the case and its units of analysis?

Is the case adequately defined (size, domain, process, subjects…)?

Is there a rationale behind the selection of subjects, roles, viewpoints, etc.?

Is the specified case relevant to validly address the research questions (construct validity)? On completion of the description of the research question(s), it is important to clarify the adopted case study setting. This section is related to the general characteristics of each case study, the “case” under investigation, and its unit(s) of analysis.

3.2.1 Overall framework

WP8 involves different units of analysis, different sectors and products, different stages of development and different geographical areas. Each partner is first asked to describe the general aspects of the case study in which they are involved, and then to clearly define the case under investigation and the unit(s) of analysis. Table 5 shows the general characteristics of each case study, including a list of involved partners. Partners have defined the following key dimensions of each case study: the geographical scope, development stage, bio-based pathways (single or multiple sectors), and the strategies to select the unit(s) of analysis. The unit(s) of analysis might be selected for theoretical purposes (based on a “logical or deductive reason dictated by the nature of the research project” (Riffe et al., 2014, p. 76), for convenience (“a census in which the population is defined by availability rather than research questions” (Riffe et al., 2014, p. 75), randomly chosen or based on a stratified sampling. Table 5 –Draft of case studies in BioMonitor project

Criteria

Ne

w w

oo

d-

bas

ed

pro

du

cts

Bio

-re

fin

eri

es

Ch

em

ical

ind

ust

ry

Ad

dit

ive

s in

th

e F

ee

d

Ind

ust

ry

Cir

cula

rity

in

the

bio

-bas

ed

pac

kagi

ng

ind

ust

ry

Ital

y an

d

Spai

n, L

atvi

a

and

Slo

vaki

a

Van

guar

d

Init

iati

ve

Participant

Subtask leader EFI TUM NOVA TI NOVA WU UCSC Other partecipants STT UCSC DTT DTT CITA TUM

WU CBS UCSC NOVA SUA WEcR



LLU Geographical scope

Multiple Member state level X X X X X Single Member State X X X X

Multiple regions across Member States X X

Development stage Mature X X X X

New bio-based products or industries X X X X X Dynamic (new to mature) X X X X X

Bio-based pathways

Single commodity X Multiple commodities X X X X X X X

Sampling strategy

Theoretical X X X X X Convenience X X X X X

Random X Stratified X

3.2.2 Selecting cases and units

Once the general characteristics of each case study are made clear, partners are asked to define the case under investigation and the chosen unit of analysis. The ambiguity between the case and the unit of analysis represents an imperative dilemma of unfortunate consequences to the case study approach. For this reason, we specify the following: in our approach, a "case" coincides with a studied phenomenon and the unit of analysis is the level of abstraction lower than the "case", a single entity to which the researcher refers in his analysis to investigate the phenomenon under study and to provide the information necessary to answer the research question(s). Table 6 defines the cases units of analysis selected by each case study in WP8.

Table 6 – Verification of case and unit(s) of analysis

Case Study Case and Unit(s) of analysis


The case study on new wood-based products has not yet been defined in detail. Very likely, the case study will build on the approach developed by Hurmekoski et al. (2018), who explored new wood-based products in Finland, Sweden, United States and Canada. The current case study will focus on multiple European countries to get a picture of different product types (e.g., construction, packaging, textiles, chemicals, polymers) that may be of more or less importance in different parts of Europe. The Unit will be new wood-based products that will be identified based on (technical) literature and interviews, based on a set of criteria. These criteria could include a TRL score of at least six (out of nine, i.e., a time-to-market of less than five years, their volume potential, market attractiveness (competition and competitiveness), cost competitiveness; and sustainability aspects (Hurmekoski et al. 2018).

8.2.2 - Bio-refineries The unit of analysis will be various types of bio-refineries across EU member states.




The unit of analysis is a sub-sector of the EU chemical industry. This sub-sector is not yet defined. The definition of the sub-sector could be based on the type of bio-based feedstock used (examples: the oleochemical industry or the sugar-based industry) or the type of products produced (examples: the bio-based plastics industry or the bio-based surfactants industry).


Dynamics in additive in the Feed Industry: Production of feed takes place to a great extent on agricultural used land, domestically and imported like in the case of soybeans. Feed is produced and used on farm, or processed in the feed industry. Feed additive can be applied in the feed industry or on farm, both to improve efficiency in animal . If additives are applied, use and imports of feed crops (esp. soybeans) per unit can be reduced, utilization of domestically grown fodder can be increased due to improved efficiency. That way leakage of nitrate can be reduced as well. For production of feed additives both fossil-based and bio-based feedstock can be used. Currently, the fossil-based additives dominate the market. The case will deal with companies which are managing aspects along the chain: companies producing feed additives, feed processing industries, farms with animal husbandry and feed production.


The units of analysis are selected EU sectors for packaging materials. These will include fully, partially and non-bio-based packaging materials. The exact packaging materials to be studies are not yet defined.


The units of analysis in this case study are four pilot EU Member States. These countries are selected by theoretical criteria to test, validate and fine-tune the model, which represents the “case” under investigation.


Our research focuses on understanding the dynamics, internal structure and resilience of emerging bio-based supply chains. Within a multiple-case study, two biobased supply chains are purposely selected and investigated. One case concerns an emerging supply chain regarding the production of PHA from the Organic Fraction of Municipal Solid Waste (OFMSW). The other is a mature supply chain dedicated to the production of bioplastics and bioproducts of renewable origins conceived to protect soil and water.

3.3 Crafting instrument

To-do list: • Identify sources of evidence; • Identify, select and keep contact with potential stakeholders; • Discuss arrangements for maintaining the confidentiality of data, sources and reports; • Define procedures governing field procedures; • Select or develop instruments or standardized procedures (Stake, 1995);

Checklist items:

Does the design involve data from multiple sources (data triangulation), using multiple methods (method triangulation)?

Is the integrity of individuals/organizations considered?

Are measurement instruments and procedures well defined (measurement definitions, interview questions)?

Are the planned methods and measurements enough to fulfil the objective of the study?

Has the study design been approved by WP8 partners, and has formal consent been obtained from individuals and organizations?

Another aspect that emerges from all the analysed case study methodologies is the importance of defining the data sources in advance and applying a technique known as data triangulation. Data triangulation suggests using multiple sources aimed at corroborating the same finding (Yin, 2014). Yin (2014) describes six sources of evidence, highlighting the strengths and weaknesses of each one (Table 7).



Table 7 – Six Sources of Evidence: Strengths and Weaknesses

Type of evidence Strenghts Weaknesses

Documentation

Stable - can be reviewed repeatedly

Unobtrusive - not created as a result of the case study

Specific - can contain the exact names, references, and details of an event

Broad - can cover a long period, many events, and many settings

• Retrievability-can be difficult to find • Biased selectivity, if the collection is

incomplete • Reporting bias—reflects (unknown)

the bias of any given document's author

Access-may be deliberately withheld

Archival Records • [same as those for documentation] • Precise and usually quantitative

• [same as those for documentation] • Accessibility due to privacy reasons

Interviews

• Targeted - focuses directly on case study topics

• Insightful - provides explanations as well as personal views (e.g. perceptions, attitudes, and meanings)

• Bias due to poorly articulated questions • Response bias • Inaccuracies due to poor recall • Reflexivity - interviewee gives what interviewer

wants to hear

Direct observations • Immediacy - covers actions in real time • Contextual - can cover the case's context

• Time-consuming • Selectivity - broad coverage difficult without a

team of observers • Reflexivity - actions may proceed differently

because they are being observed • Cost - hours needed by human observers

Participant- observation

• [same as those for direct observations] • Insightful into interpersonal behaviour and

motives

• [same as those for direct] • Bias due to participant-observer's manipulation

of events

Physical artefacts • Insightful into cultural features • Insightful into technical operations

• Selectivity • Availability

Source: (Yin, 2014, p.106)) – Adapted by authors

The combination of qualitative and quantitative sources is particularly important, as highlighted by Eisenhardt (1989):

"...the combination of data types can be highly synergistic. Quantitative evidence can indicate relationships which may not be salient to the researcher. It also can keep researchers from being carried away by vivid, but false, impressions in qualitative data, and it can bolster findings when it corroborates those findings from qualitative evidence. The qualitative data are useful for understanding the rationale or theory underlying relationships revealed in the quantitative data or may suggest directly theory which can then be strengthened by quantitative support." (Eisenhardt, 1989, p. 538)

Table 8 shows the selected source of evidence according to the different outputs expected for each case study.



Table 8 –– Source of evidence

Case Study Lessons learnt and best practices for

data collection Test and validation of new bioeconomy

indicators Providing data for models in BioMonitor

Model Toolbox Validation of outcomes of BioMonitor

Model Toolbox


The case study on new-wood based products will require projection methods, as the products of the future cannot be known. For that purpose, the case study will need to rely on information available from the literature and experts (interviews, workshop). To identify and validate the selection of new products and their possible value chains, the study team will approach national research centres, as well as representatives from the forest-based industries.

8.2.2 - Bio-refineries

Direct observation and survey

Interviews with experts of selected industries;

Archival records (meaning here statistical data, literature, market studies, press releases, company statements, etc. for the selected countries)

The model output will be compared with real world data.


Direct observation

Interviews with experts of the selected industries

Archival Records (meaning here literature, market studies, press releases, company statements, etc. for the selected bio-based industries)

Direct observation The source of evidence would be the model output and the cross-checking of this output with the expectations.




Plausibility cross-checked with experts and researchers

Explorative literature research

(Face-to-face) interviews with companies and farms willing to collaborate with us in this case study

Interviews with Stakeholders

The case study on additives will require simulation methods, as measures may affect developments. For that purpose, the case study will need to rely on information available from the literature and experts (interviews, workshop). Feedback with modellers developing scenarios under WP5

Serves as input


Plausibility cross-checked with experts and researchers

Interviews with experts of the selected industries

Archival Records (meaning here literature, market studies, press releases, company statements, etc. for the selected bio-based industries)

Direct observation The source of evidence would be the model output and the cross-checking of this output with the expectations


Data and information are tested when developing a MFM with sufficient bioeconomy detail. The MFM should serve as data input for new innovative bio-based sectors in the models as these are missing in current state of models. Source of evidence is the model output and the cross-checking with expectations of stakeholders.


Semi-structured interviews, focus group discussion and company surveys.

Empirical data will be integrated with secondary data from documents, reports, and bibliographic sources

Interviews and surveys Source of evidence would be the model output



3.3.1 Identify and select stakeholders

From the previous section, all WP8 case studies include direct interaction with relevant stakeholders through interviews, one of the most important sources of empirical information to increase the comprehension of the phenomena under investigation and produce more robust results. It is important to incorporate the expertise and knowledge of all possible actors on the case to improve the external validity of the analysis. Once relevant stakeholders are identified during Task 8.2, each partner is asked to indicate which categories are included in their own case study (e.g, governmental actors, policymakers, value chain actors, statistical officers, academics and R&D) and list the stakeholders engaged in a WP8 Stakeholder Database. To involve the stakeholders, case studies are often related to field works. The procedures and permissions for gaining information, how field visits, interviews and workshops were scheduled, will be described in the final report. These procedures also cover the protection of respondents and limiting liabilities. To contact and mark the stakeholder in the WP8 Stakeholders Database, the following procedure was developed in agreement with WP7 (“Training and overall stakeholder engagement”): Step 1: check if the stakeholder you want to contact is listed or not in the WP8 Stakeholder database.

If already listed, refer to the WP8 contact partner providing the information requested in Step 2 point B.

If not listed, please add the stakeholder in WP8 database and include yourself as WP8 contact partner. Then, check WP7 database and write the WP7 contact partner in the WP8 database, if any.

Step 2: If you are the WP8 contact partner, according to what reported in column “J: Action”:

A. If the cell says: “Ok” or “Update Database WP7” and you are the WP8 contact partner you are free to contact the stakeholder the way you prefer.

B. If the cell says: “Check with WP7 PPC”, then you need to contact the PPC providing the following information:

a. Do you have direct contact or do you need PPC support? b. To whom (position and role) would you like to refer? c. What type of interaction would you like to have? (e.g. mail, phone, personal

meeting..., and how many times) d. What type of information would you like to ask?

Step 3: PPC will assess the above information and answer accordingly. The answers could range from “feel free to contact them directly” to “I will contact them for you and let you know if they are willing to collaborate on these aspects”.



3.4 Linkages with indicators in BioMonitor

One output common to all of the case studies is the validation of indicators and data collection. A list of indicators has been developed in WP1. Table 9 highlights those indicators for which partners reasonably believe they can perform validation and a data collection test (please refer to Deliverable 1.1 for more information about indicators for the BioMonitor Project).

Table 9– Verification Indicators covered by each case study

Criteria

Ne

w w

oo

d-

bas

ed

pro

du

cts

Bio

-re

fin

eri

es

Ch

em

ical

ind

ust

ry

Dyn

amic

s o

f B

io-

bas

ed

Ad

dit

ive

s

in t

he

Fe

ed

Ind

ust

ry

Cir

cula

rity

in t

he

bio

-bas

ed

p

acka

gin

g

ind

ust

ry

Ital

y an

d S

pa

in,

Latv

ia a

nd

Slo

vaki

a

Van

guar

d

Init

iati

ve

1. Food and nutrition security

Availability of food X X

Access to food X

Utilization X X

Stability X

2. Sustainable natural resources management

Sustainability threshold levels for Bioeconomy Technologies

X

Biodiversity X

Land cover X

Primary Biomass production X X

Sustainable resource use X X X

3. Dependence on non-renewable resources

Bio-energy replacing non-renewable energy

X

Bio-material replacing non-renewable resources

X X X (partly) X X X

Biomass self-sufficiency rate X X X

Material use efficiency X X X X X X

Certified bio-based products X X X

4. Mitigating and adapting to climate change

Greenhouse gas emissions X X X X

Climate footprint X X

Climate change adaptation X

5. Employment and economic competitiveness

Innovation X X X X X X

Investments X X X X X X

Value Added of the bioeconomy sectors

X X X X X X

Comparative advantage X X X X X X

Production and consumption of non-food and feed bio-based products

X X X X X

Import and export of bioeconomy raw materials and products

X X X X X

Employment X X X X X

Policies X X X X X X



4 Phase two: Data collection and Analysis

The purpose of this section is to ensure uniformity and interactivity in data collection and analysis, stress the importance of the definition and description of the context, and facilitate them both within and across case studies by recommending common procedures when possible (e.g, interview).

4.1 Context analysis

To-do list: • Define and describe the context of the case;

A case study investigates a contemporary phenomenon in depth and in its real-world context (Yin, 2014). The context of the investigated phenomenon is important as it is its unit of analysis. The description of the context will help to bound the case, define the units and determine the scope of the data collection. Researchers cannot disregard a detailed description of the context to ensure generalizability of the findings. A priority of the protocol is to ensure that the information provided by context analysis ensures a comparative analysis of individual case studies by identifying common and diverging patterns of the different outputs (Task 8.3: Feedback and recommendations). All case studies need to include a context analysis during Task 8.2 to create a deeper understanding of the different forces of the local ecosystem. A descriptive framework of the main forces influencing the case and its unit(s) is required. Knowing these forces and understanding how they affect the case and its unit(s) will provide important information for the monitoring and impact assessment activities in the BioMonitor project, as mentioned in D1.1. Driving forces can be grouped under themes, respectively supply drivers and demand drivers, resource availability and the measures of governments that influence the development of the bioeconomy (please refer to Deliverable 1.1 for more information).

4.2 Data collection

To -do list: • Define the data to be collected (check D2.1); • Define how the data will be collected (check D3.1) and stored (check D10.1); • Make preliminary observations, reconsider issues and improve the data collection; • Collect data (observations, interview, surveys, etc.); • Arrange the evidence in a systematic order;

Checklist items: • Is data collected according to the case study protocol? • Is data recorded to enable further analysis? • Are sensitive results identified (for individuals, the organization or the project)?



• Are the data collection procedures well traceable? • Does the collected data provide the ability to address the research question?

Deliverable 3.1 defines the status quo of bioeconomy data collection methodologies and recommendations. Therefore, the protocol highlights only some aspects of data collection through interviews, which all case studies include as a source of evidence.

4.2.1 Interview

Interviews can be conducted as structured, unstructured or semi-structured interviews. The structured interview method includes pre-determined questions with a very limited margin of flexibility to deviate from the sequence of questions. Attention in this method should be paid especially to the formulation of questions since the potential bias of the researcher could affect the design of the questions. Opposite to structured interviews is the informal method of unstructured interviewing: interviewers do not know in advance the questions, but instead they are developed, generated and adapted during what can be considered informal conversations on the field. The process of analysing the responses to these follow-up questions takes much longer than structured interviews and much more attention must be paid when repeated with several respondents to ensure the comparability of the responses. Finally, researchers may adopt a semi-structured interview, an intermediate approach between structured and unstructured interviews. This form of interview resembles guided conversations rather than structured questions. It is characterized by a fluid flow of questions prepared in a consistent and systematic way to guide and direct the conversation through identified topics that the interviewer wants to investigate. Regardless of the chosen method, all respondents should, as far as possible, provide informed consent to participate in the interview and, possibly, to being tape-recorded. The main challenges while conducting interviews regard: (i) the context, background, circumstances and experience of the respondents, which can influence the participants, who focus only on local specificities and introduce potential issues to generalize the gathered evidence (Dodd et al., 2018); (ii) interviews may not encompass all possible key stakeholders related to the case to allow any generalizations of findings (Lazarevic and Martin, 2018); (iii) the researcher may inadvertently bias the data collected due to the question design; (iv) respondents may be hesitant to provide certain information (know-how, data on investments made by firms) (Portillo-Tarragona et al., 2018).



4.3 Analysing the data

To-do list: • Seek linkages between research question(s) and data collected, establishing a chain of

evidence (Construct Validity); • Start to create a list of codes and then generate categories (Saldaña, 2013); • Write about categories/pattern codes to explain their significance and test them against

new data; • Review data, gather new data, deliberately seek disconfirmation of findings (Stake, 1995);

Checklist items:

• Is the methodology defined, including roles and review procedures? • Is a chain of evidence shown with traceable inferences from data to research questions

and existing theory? • Are alternative perspectives and explanations used in the analysis? • Are there clear conclusions from the analysis, including recommendations for

practice/further research? • Are threats to the validity analysed in a systematic way and countermeasures taken?

(Construct, internal, external, reliability) Both quantitative and qualitative analysis is applied in the different case studies of BioMonitor (e.g., input-output analysis, econometric analysis, life cycle assessment, network analysis). To meet the flexibility required by the WP8, the protocol does not force a common methodology in carrying out the analysis, which would be rather counterproductive given the variety of analysis and research questions proposed. However, within any general strategy, to consolidate the meaning of the data collected partners should consider using a coding process to arrange the evidence in a systematic order. It is essential that, beyond the analysis carried out during Task 8.2, a clear chain is maintained linking the research questions with the evidence collected. Coding is “only the initial step toward an even more rigorous and evocative analysis and interpretation for a report” (Saldaña, 2013, p. 8). It is a method that enables the researcher to organize, segregate, and link data (Grbich, 2007) that are coded into categories or “families” because they share some characteristic – the beginning of a pattern (Saldaña, 2013). Coding is thus a method that “leads you from the data to the idea, and from the idea to all the data pertaining to that idea” (Richards and Morse, 2007, p. 137).

4.3.1 What is a code?

A code is the process that assigns a summary, salient, capturing and evocative attribute to a portion of data (Saldaña, 2013). A code is a construct generated by the researcher for subsequent purposes of pattern detection, categorization, theory building and other analytical processes (Saldaña, 2013). Codes are essential elements that actively facilitate the development of categories and their analysis, allow to interact with the great diversity in the form of collected data (Saldaña, 2013),



and allow to report a chain of evidence between the final conclusions reached and the data collected.

4.3.2 The Mechanics of Coding

Saldaña (2013) provides a manual for the coding process, discussing code and coding functions, profiling a repertoire of coding methods (especially for qualitative data analysis), and providing readers with sources, examples and exercises for coding. This section refers to this manual, which we suggest to partners as reference for coding and further analysing data. The coding usually follows the ideal and simplified scheme illustrated by Saldaña (2013) in Figure 3.

Figure 3 Streamlined codes-to-theory model for qualitative inquiry

Source: Saldaña (2013)

The first step concerns the preparation of data for encoding (such as transcription of recordings and participant observation field notes). In the preparation of the data, it is important to organize (transcribe, convert, manage, catalogue and store) the new data collected and not wait until the end of the entire data collection to do so. When transcribing recorded interviews or archiving documents collected on site, it is important to keep track of any preliminary significant words or phrases for future coding. Do not rely on your memory for future analysis. Get your thoughts documented in some way.



The overlap of data analysis with data collection is another important aspect to keep in mind. It allows researchers to take advantage of the flexibility of data collection, given the possibility to make changes during the data collection process. Such adjustments may refer, for example, to adding an interview protocol or question to a questionnaire.

“If a new data collection opportunity arises or if a new line of thinking emerges during the research, it makes sense to take advantage by altering data collection, if such an alteration is likely to better ground the theory or to provide new theoretical insight. This flexibility is not a license to be un-systematic. Rather, this flexibility is controlled opportunism in which researchers take advantage of the uniqueness of a specific case and the emergence of new themes to improve resultant theory" (Eisenhardt, 1989, p. 539)

After the preparation of collected data, researchers can start the coding process. Coding enables to organize and group similarly coded data into categories because they share some characteristic – the beginning of a pattern. Varying forms of patterns are characterized by:

- similarity (things happen the same way) - difference (they happen in predictably different ways) - frequency (they happen often or seldom) - sequence (they happen in a certain order) - correspondence (they happen in relation to other activities or events) - causation (one appears to cause the other)

Some categories may contain encoded data groups that deserve further refinement in subcategories. When the main categories are compared and consolidated in various ways, you can continue the analytical strategy chosen to progress towards an increasingly abstract and general theme/concept that allows to test or build a theory. Computer-assisted qualitative data analysis software (CAQDAS) can help to code and categorize large amounts of data. For instance, CAQDAS programs, by default, will maintain a list of codes previously created for the project and periodically update it. Several major CAQDAS programs that are suggested in the manual are:

- ATLAS.ti: www.atlasti.com - HyperRESEARCH: www.researchware.com - MAXQDA: www.maxqda.com - NVivo: www.qsrinternational.com - Transana: www.transana.org

The software, however, will not complete the analysis on its own; it may serve just as an able assistant and reliable tool (Yin, 2014).

http://www.atlasti.com/

http://www.researchware.com/

http://www.maxqda.com/

http://www.qsrinternational.com/

http://www.transana.org/



5 Phase three: Reporting

5.1 Case study report

To-do list: • Review the template case study report (construct validity)(Yin, 2014); • Describe the setting extensively; • Describe the context extensively; • Describe the interactivity of data collection and analysis extensively; • Report conclusions, best practices, feedback, and recommendations; • Revise and disseminate reports;

Checklist items:

• Are the case and its units of analysis adequately presented? • Are the objective, the research questions and corresponding answers reported? • Are related theory and hypotheses clearly reported? • Are the data collection procedures presented, with relevant motivation? • Are the raw data presented sufficient (e.g.,., real life examples, quotations)? • Are the analysis procedures clearly reported? • Are threats to validity analyses reported along with countermeasures taken to reduce

threats? • Are ethical issues reported openly (personal intentions, integrity issues, confidentiality) • Does the report contain conclusions, implications for practice and future research? • Does the report give a realistic and credible impression? • Is the report suitable for its audience, easy to read and well structured?

The case study report is one of the most important aspects for the successful completion of a case study. Each case study will be reported following a common structure. Choosing a common scheme facilitates the work of task 8.3 (Feedback and recommendations) while making it more efficient. The reports correspond to the following deliverables:

• D8.2 Report on the case study “New wood-based products” (M30). • D8.3 Report on case study “Bio-refineries sector” (M30). • D8.4 Report on the case study “Chemical industry” (M30). • D8.5 Report on the case study “Dynamics in bioeconomy: from infant products to large-

scale production” (M30). • D8.6 Report on the case study “Circularity in the bio-based packaging industry” (M30). • D8.7 Report on the case study “New bio-based economy sectors Latvia and Slovakia,

Italy and Spain” (M48). • D8.8 Report on the case study “The Bioeconomy Pilot from the Vanguard Initiative”

(M30).



The case study report provides a detailed description of the research problem and proposed design (research question, case, unit of analysis), data collection, context analysis and the adopted data analysis. The outline is provided in the following table.

Table 10 – Case studies report outline

Section Illustrative Content

Justification of research approach

A statement of why the case method was adopted, why it is appropriate, together with a clear explanation of why that unit(s) of analysis were chosen;

Research Question(s) Description of the Research question(s);

Design Definition of the case, type of design, development stage, bio-based pathways, and sampling strategy; the (logical) connection between the research question(s) and the unit(s) of analysis;

Context Description of the contextual factors influencing the case and the unit(s) of analysis;

Data Collected

Emphasis on how the data provided an "up close" and "in-depth" coverage of the case(s); presentation of the case study protocol and how it was used; list of sources of evidence used in order of importance; further detail about specific items within each source (e.g., numeric profile of interviewees in tabular form or an appended list of documents reviewed); how the data were verified (e.g., triangulation methods);

Analysis Method Description of the analytic approach;

Caveats about study Intrinsic deficiencies may be involved in the design and analysis. How the deficiencies may have affected the outcome;

Feedback (to other WPs) Unexpected difficulties that were encountered and how they might have affected the data collection; possible suggestions to improve the data collection methods and modelling framework;

Recommendations and best practices

Where and how the methodology developed in BioMonitor can be successfully applied also considering the improvement gained from the feedback.

Source: Yin (2014) – Adapted by authors

The reports will include two specific sections on feedback and recommendations. In the "Feedback" section, partners involved in Task 8.3 (DTT, EFI, LLU, NOVA, STT, SUA, TI, TUM, UCSC, WEcR) will analyse the results of the respective case study and discuss possible suggestions for improving data collection methods and the modelling framework. This discussion will then be extended to stakeholders involved in WP7 for further validation. This will result in a feedback report shared with previous WPs (subtask 8.3.1). In “Recommendations and best practices” section, recommendations specifically targeted to the end users of BioMonitor indicators, data collection, and modelling frameworks are elaborated. This section will facilitate the work of subtask 8.3.2 to extract generalizations on where and how the methodology developed in BioMonitor can be successfully applied also considering the improvement gained from the feedback process of Subtask 8.3.1. This will allow defining (i) under which conditions the methodology developed in BioMonitor can be applied and replicated in a successful way considering different development stages of technologies, geographical scopes, bio-based pathways, supply chain stages, and statistical schemes and (ii) good practices in the implementation of the methodology developed in BioMonitor.



6 Conclusions

By facilitating a common understanding of the most relevant aspects of case study research, this protocol allows all partners to use common concepts, the same terminology and move in the same methodological direction while maintaining the flexibility required in WP8 due to the heterogeneity of the case studies. The protocol represents a key factor for the validity, reliability and transparency within WP8, allowing each partner to have an overview of what everybody else is doing and how. Partners will strive to apply this common methodology according to the different outputs. In this way, the protocol ensures that (i) the mission and goals of each case study reflect the interests of WP8, (ii) the comparative analysis of individual case studies is facilitated (Task 8.2) and (iii) feedback on possible improvements and recommendations for end-users are significant and consistent (Task 8.3). During the preparation of this protocol, it was possible to further screen the pre-selected and proposed case studies topics to verify that they met the specific requirements of the operational protocol and to detail the scope of each case study, as required by subtask 8.1.2 - Verification of case studies. Finally, it is important to underline that the protocol will be regularly updated and revised to ensure its effective and efficient use. In order to facilitate updating, we have structured the protocol in such a way that the common tools, which should be used by the partners, are set out in the appendix.



References

Bentsen, N.S., Nilsson, D., Larsen, S., 2018. Agricultural residues for energy - A case study on the influence of resource

availability, economy and policy on the use of straw for energy in Denmark and Sweden. Biomass and Bioenergy

108, 278–288. https://doi.org/10.1016/j.biombioe.2017.11.015

Bowd, R., Quinn, N.W., Kotze, D.C., Guilfoyle, M.J., 2018. A systems approach to risk and resilience analysis in the

woody-biomass sector: A case study of the failure of the South African wood pellet industry. Biomass and

Bioenergy 108, 126–137. https://doi.org/10.1016/j.biombioe.2017.10.032

Brereton, P., Kitchenham, B.A., Budgen, D., Li, Z., 2008. Using a Protocol Template for Case Study Planning. EASE (Vol.

8, pp. 41-48). 8, 41–48. https://doi.org/10.1002/bab.1470

Burliai, A., Ryabchenko, O., Bielik, P., Burliai, O., 2018. Energy Security Facets: Verification of Horticultural Wooden

Waste Potential With Bioenergy Development Purpose. J. Secur. Sustain. Issues 8, 55–67.

https://doi.org/10.9770/jssi.2018.8.1(5)

Carter, E., Shan, M., Zhong, Y., Ding, W., Zhang, Y., Baumgartner, J., Yang, X., 2018. Development of renewable,

densified biomass for household energy in China. Energy Sustain. Dev. 46, 42–52.

https://doi.org/10.1016/j.esd.2018.06.004

Charmaz, K., 2001. Grounded theory. In R. M. Emerson (Ed.), in: Contemporary Field Research: Perspectives and

Formulations. Waveland Press, Prospect Heights, IL, pp. 335–52.

Dewick, P., Foster, C., 2018. Focal Organisations and Eco–innovation in Consumption and Production Systems. Ecol.

Econ. 143, 161–169. https://doi.org/10.1016/j.ecolecon.2017.07.012

Dodd, T., Orlitzky, M., Nelson, T., 2018. What stalls a renewable energy industry? Industry outlook of the aviation

biofuels industry in Australia, Germany, and the USA. Energy Policy 123, 92–103.

https://doi.org/10.1016/j.enpol.2018.08.048

Eisenhardt, K.M., 1989. Building Theories from Case Study Research. Acad. Manag. Rev. 14, 532–550.

https://doi.org/10.5465/AMR.1989.4308385

Fuentes, A., García, C., Hennecke, A., Masera, O., 2018. Life cycle assessment of Jatropha curcas biodiesel production:

a case study in Mexico. Clean Technol. Environ. Policy 20, 1721–1733. https://doi.org/10.1007/s10098-018-

1558-7

Grbich, C., 2007. Qualitative data analysis: An introduction. SAGE, Thousand Oaks, CA.

Grünbaum, N.N., 2007. Identification of ambiguity in the case study research typology: What is a unit of analysis?

Qual. Mark. Res. 10, 78–97. https://doi.org/10.1108/13522750710720413

Laso, J., Margallo, M., García-Herrero, I., Fullana, P., Bala, A., Gazulla, C., Polettini, A., Kahhat, R., Vázquez-Rowe, I.,

Irabien, A., Aldaco, R., 2018. Combined application of Life Cycle Assessment and linear programming to evaluate

food waste-to-food strategies: Seeking for answers in the nexus approach. Waste Manag. 80, 186–197.

https://doi.org/10.1016/j.wasman.2018.09.009



Lazarevic, D., Martin, M., 2018. Life cycle assessment calculative practices in the Swedish biofuel sector: Governing

biofuel sustainability by standards and numbers. Bus. Strateg. Environ. 27, 1558–1568.

https://doi.org/10.1002/bse.2214

Maimbo, H., Pervan, G., 2005. Designing a Case Study Protocol for Application in IS research, in: PACIS. pp. 1281–1292.

Mengistu, T.W., Gupta, S., Birner, R., 2018. Analysis of maize biomass use in Ethiopia and its implications for food

security and the bioeconomy. Food Secur. 10, 1631–1648. https://doi.org/10.1007/s12571-018-0865-5

Monteleone, M., Cammerino, A.R.B., Libutti, A., 2018. Agricultural “greening” and cropland diversification trends:

Potential contribution of agroenergy crops in Capitanata (South Italy). Land use policy 70, 591–600.

https://doi.org/10.1016/j.landusepol.2017.10.038

Patricio, J., Axelsson, L., Blomé, S., Rosado, L., 2018. Enabling industrial symbiosis collaborations between SMEs from a

regional perspective. J. Clean. Prod. 202, 1120–1130. https://doi.org/10.1016/j.jclepro.2018.07.230

Poku, A.G., Birner, R., Gupta, S., 2018. Is Africa ready to develop a competitive bioeconomy? The case of the cassava

value web in Ghana. J. Clean. Prod. 200, 134–147. https://doi.org/10.1016/j.jclepro.2018.07.290

Portillo-Tarragona, P., Scarpellini, S., Moneva, J.M., Valero-Gil, J., Aranda-Usón, A., 2018. Classification and

measurement of the firms’ resources and capabilities applied to eco-innovation projects from a resource-based

view perspective. Sustain. 10. https://doi.org/10.3390/su10093161

Qi, X., Wang, R.Y., Li, J., Zhang, T., Liu, L., He, Y., 2018. Ensuring food security with lower environmental costs under

intensive agricultural land use patterns: A case study from China. J. Environ. Manage. 213, 329–340.

https://doi.org/10.1016/j.jenvman.2018.02.048

Ranta, V., Aarikka-Stenroos, L., Mäkinen, S.J., 2018. Creating value in the circular economy: A structured multiple-case

analysis of business models. J. Clean. Prod. 201, 988–1000. https://doi.org/10.1016/j.jclepro.2018.08.072

Richards, L., Morse, J.M., 2007. Readme first for a user’s guide to qualitative methods. SAGE, Thousand Oaks, CA.

Riffe, D., Lacy, S., Fico, F., Watson, B., 2014. Analyzing media messages: Using quantitative content analysis in

research, 3nd ed, Routledge. New York, NY. https://doi.org/10.17654/ms103101535

Runeson, P., Höst, M., 2009. Guidelines for conducting and reporting case study research in software engineering.

Empir. Softw. Eng. 14, 131–164. https://doi.org/10.1007/s10664-008-9102-8

Saldaña, J., 2013. The coding manual for qualitative researchers, 2nd ed. SAGE publications.

Schaller, L., Targetti, S., Villanueva, A.J., Zasada, I., Kantelhardt, J., Arriaza, M., Bal, T., Fedrigotti, V.B., Giray, F.H.,

Häfner, K., Majewski, E., Malak-Rawlikowska, A., Nikolov, D., Paoli, J.C., Piorr, A., Rodríguez-Entrena, M., Ungaro,

F., Verburg, P.H., van Zanten, B., Viaggi, D., 2018. Agricultural landscapes, ecosystem services and regional

competitiveness—Assessing drivers and mechanisms in nine European case study areas. Land use policy 76,

735–745. https://doi.org/10.1016/j.landusepol.2018.03.001

Stake, R.E., 1995. The Art of Case Study Research. SAGE. https://doi.org/10.2307/329758

Yin, R.K., 2014. Case Study Research Design and method, fifth edit. ed. SAGE publications.



Appendix

Appendix A

Table 11 – Researcher’s checklist

Phase one: Case study design 1. Are clear objectives, preliminary research questions, hypotheses (if any) defined in advance? 2. Is the theoretical basis—relation to existing literature or other cases—defined? 3. Are the authors’ intentions with the research made clear? 4. What is the case and its units of analysis? 5. Is the case adequately defined (size, domain, process, subjects…)? 6. Is there a rationale behind the selection of subjects, roles, artifacts, viewpoints, etc.? 7. Is the specified case relevant to validly address the research questions (construct validity)? 8. Does the design involve data from multiple sources (data triangulation), using multiple methods (method

triangulation)? 9. Is the integrity of individuals/organizations taken into account? 10. Are measurement instruments and procedures well defined (measurement definitions, interview questions)? 11. Are the planned methods and measurements enough to fulfill the objective of the study? 12. Is the study design approved by WP8 partners, and has informed consent obtained from individuals and

organizations? Phase two: Data collection and Analysis

13. Is data collected according to the case study protocol? 14. Is data recorded to enable further analysis? 15. Are sensitive results identified (for individuals, the organization or the project)? 16. Are the data collection procedures well traceable? 17. Does the collected data provide the ability to address the research question? 18. Is the analysis methodology defined, including roles and review procedures? 19. Is a chain of evidence shown with traceable inferences from data to research questions and existing theory? 20. Are alternative perspectives and explanations used in the analysis? 21. Are there clear conclusions from the analysis, including recommendations for practice/further research? 22. Are threats to the validity analysed in a systematic way and countermeasures taken? (Construct, internal,

external, reliability) Phase three: Reporting

23. Are the case and its units of analysis adequately presented? 24. Are the objective, the research questions and corresponding answers reported? 25. Are related theory and hypotheses clearly reported? 26. Are the data collection procedures presented, with relevant motivation? 27. Is sufficient raw data presented (e.g., real life examples, quotations)? 28. Are the analysis procedures clearly reported? 29. Are threats to validity analyses reported along with countermeasures taken to reduce threats? 30. Are ethical issues reported openly (personal intentions, integrity issues, confidentiality) 31. Does the report contain conclusions, implications for practice and future research? 32. Does the report give a realistic and credible impression? 33. Is the report suitable for its audience, easy to read and well structured?

Source: Runeson and Höst (2009) – Adapted by authors



Appendix B

Table 12 – To-do list for the researcher

To-do list for Researcher

Phase one: Case study design

1. Define research question(s) and objective(s); 2. Identify previous case studies and review the literature on the topic; 3. Define common WP8 research question(s); 4. Share and discuss within WP8 and WP leaders the research question(s); 5. Describe the overall framework of the case study; 6. Define case under investigation and the chosen unit(s) of analysis; 7. Share and discuss within WP8 and WP leaders adopted case study setting; 8. Identify sources of evidence; 9. Identify, select and keep contact with potential stakeholders; 10. Discuss arrangements for maintaining the confidentiality of data, sources, reports; 11. Define procedures governing field procedures; 12. Select or develop instruments or standardized procedures (Stake, 1995);

Phase two: Data collection and Analysis

13. Define and describe the context of the case; 14. Define the data to be collected (check D2.1); 15. Define how the data will be collected (check D3.1) and stored (check D10.1); 16. Make preliminary observations, reconsider issues and improve the data collection; 17. Collect data (observations, interview, surveys, etc.); 18. Arrange the evidence in a systematic order; 19. Seek linkages between research question(s) and data collected, establishing a chain of evidence

(Construct Validity); 20. Start to create a list of codes and then generate categories (Saldaña, 2013); 21. Write about categories/pattern codes to explain their significance and test them against new data; 22. Review data, gather new data, deliberately seek disconfirmation of findings (Stake, 1995);

Phase three: Reporting

23. Review the template case study report (construct validity)(Yin, 2014); 24. Describe the setting extensively; 25. Describe the context extensively; 26. Describe the interactivity of data collection and analysis extensively; 27. Report conclusions, best practices, feedback, and recommendations; 28. Revise and disseminate reports;



Appendix C



Glossary

- Case: “the main subject of study in a case study—usually a concrete entity (e.g., a person, organization, community, program, process, policy, practice, or institution, or an occurrence such as a decision); totally abstract “cases” (e.g., arguments, claims, or propositions) can pertain to all social science methods and may be less distinctive as cases for case studies” (Yin, 2014).

- Case study: “a study that investigates a contemporary phenomenon in depth and in its

real-world context” (Yin, 2014).

- Chain of evidence: “the links—showing how findings come from the data that were collected and in turn from the guidelines in the case study protocol and from the original research questions—that strengthen the reliability of a case study’s research procedures” (Yin, 2014).

- Code: “Most often a researcher-generated word or short phrase that symbolically assigns a summative, salient, essence-capturing, and/or evocative attribute for a portion of language-based or visual data. The data and thus coding processes can range in magnitude from a single word to a full paragraph to an entire page of text to a stream of moving images. Attributes specific meaning to each individual datum for purposes of pattern detection, categorization, and other analytic processes. Examples: SENSE OF SELF-WORTH; STABILITY; “COMFORTABLE” (Saldaña, 2013).

- Construct validity: “this aspect of validity reflects to what extent the operational measures that are studied really represent what the researchers have in mind and what is investigated according to the research questions. If, for example, the constructs discussed in the interview questions are not interpreted in the same way by the researcher and the interviewed persons, there is a threat to the construct validity”(Runeson and Höst, 2009).

- Cross-case synthesis: “a compiling of data for a multiple-case study, by examining the results for each individual case and then observing the pattern of results across the cases; stronger syntheses would have sufficient data to entertain plausible rival cross-case patterns” (Yin, 2014).

- External validity: “this aspect of validity is concerned with to what extent it is possible to generalize the findings, and to what extent the findings are of interest to other people outside the investigated case. During the analysis of external validity, the researcher tries to analyse to what extent the findings are of relevance for other cases. There is no population from which a statistically representative sample has been drawn. However, for case studies, the intention is to enable analytical generalization where the results are extended to cases which have common characteristics and hence for which the findings are relevant, i.e. defining a theory” (Runeson and Höst, 2009).



- Internal validity: “this aspect of validity is of concern when causal relations are examined.

When the researcher is investigating whether one factor affects an investigated factor there is a risk that the investigated factor is also affected by a third factor. If the researcher is not aware of the third factor and/or does not know to what extent it affects the investigated factor, there is a threat to the internal validity”(Runeson and Höst, 2009).

- Multiple sources of evidence: “data that come from different data collection sources (e.g., interviews, documents, direct observations, archives, and participant-observation), with the aim being to strengthen findings through the convergence or triangulation of the data” (Yin, 2014).

- Reliability: “this aspect is concerned with to what extent the data and the analysis are dependent on the specific researchers. Hypothetically, if another researcher later on conducted the same study, the result should be the same. Threats to this aspect of validity is, for example, if it is not clear how to code collected data or if questionnaires or interview questions are unclear” (Runeson and Höst, 2009).

- Triangulation: “the convergence of data collected from different sources, to determine the consistency of a finding” (Yin, 2014)

protocol development and verification of case...

Documents