Content analysis – Analytic procedure

4.4.1.1. Sampling frame

In order to determine the boundaries of the content analysis, given the vast data collected during the interviews, the aim of the research and its objectives were utilised. The purpose of using these

‘limits’ was to avoid being too broad, hence excluding irrelevant information which does not focus on the subject of the study. Given that the main aim of this study is to explore how

119 organised networks in Serbia, as a state undergoing democratic transition and EU accession, impact crime control and lead to potential state capture, the focus of the content analysis was on all aspects of the data that can contribute to meeting this aim. Therefore, this ‘unexplored dilemma’ was an umbrella of the focus of the content analysis.

The decision on the sampling frame also involved the determination of the time period to be included. This choice was made on the basis of the study aim and objectives. The data covering the period from 1990 to 2018 have been included, when it comes to written sources, whereas the period from 2000 to 2018 was included in the interview discussions. Court and other judiciary statistics mainly involve the period from 2012 to 2018, due to the relevance of data. The types of written sources have been selected in a manner that enables reasonable representation of the data. Namely diverse forms of written sources were used to avoid potential bias (newspapers, articles by different authors), official institutional statistics, relevant EU reports and other international reports on Serbia.

4.4.1.2. Preparation of data and categorisation

As described above, the study utilised a variety of written sources and the interviews for the purpose of data collection. Data was collected by a comprehensive desk review (official reports, media articles, statistics) and collection of written material from relevant institutions, as well as multiple interviews (semi-structured and in-depth interviews) with state officials, civil servants in relevant institutions, and the judiciary. These multiple sources were analysed in order to gain more in-depth knowledge, map the existing ‘state response’ network developed to fight against drug trafficking in Serbia and assess the results of state action in this regard. In light of the exploration of the potential state capture referred to in numerous assessments, particular attention was given to issues linked to such conclusions.

The collected data were diverse due to the variety of sources. Some interviews were transcribed, while other interviews that were not audio taped involved transcription (by the researcher) of the main verbalizations. Concurrently, the researcher has transcribed observations during the interviews, mainly as a form of reminder of the whole setting, body language and so on. Prior to

120 initiating data preparation for the analysis, the material obtained through the interviews and the written material collected have been re-read to get a general understanding of what the collected data is talking about. This process enabled the researcher to comprehend the main points or ideas arising from the information collected.

Implementation of the content analysis involves several steps. The first step refers to development of recording units. This process implies classifying the content into themes based on the objectives of the study. Content analysis theory proposes that the themes can be certain phrases, sentences or a single word (Berg, 2001). Themes are usually ideas or concepts relevant for the study. Themes entail an overall concept of an underlying meaning on an interpretative latent level. The utilisation of themes enables reflection as to how the study findings correspond to the literature and evidence based policies and whether the results are reasonable and logical.

To further specify the exact themes of the analysis, the researcher applied the objectives of the study as a form of ‘test’ whether concrete information contributes to meeting these objectives.

In practice, this means that any information obtained during the interviews or collected in the written sources that does not contribute to additional knowledge on the operation of the state

‘response network’ designed to combat drug trafficking or the extent to which the network is distorted by diverse socio-political factors and methods of social interaction, contributing to state capture, has not been included or coded. Such information, referred to as “dross” (Bengtsson, 2016; Bernard, 1995) has not been analysed. Data elucidating the functioning of criminal networks in Serbia in the context of the potential state capture were coded and included in the analysis. Likewise, information that does not add to the existing knowledge on actions in suppressing drug trafficking and its effects on the EU accession process, was classified as

“dross”. During the analysis process, this testing of the significance of data was performed by writing down research aim and questions on a paper so it can be observed to keep the focus, using specific colours for a code and writing coloured notes in the margins, enabling the researcher to stay on track during analysis.

121 Figure IV.2. Example of analysis resulting in higher levels of abstraction

There are 4 key themes formulated for the purpose of performing content analysis of data:

1. Stalled democratization/EU processes due to organised crime - corresponding to the RQ 1 To what extent can stalled transition and state capture be attributed to the level of organised crime?

2. Organised crime networks - corresponding to the RQ 2 What is the extent of organised crime and drug trafficking in Serbia?

3. Effectiveness of state response network addressing organised crime - corresponding to the RQ 3 What are the structural holes that enable endurance of organised crime in Serbia?

4. Serbia as a captured state - corresponding to the RQ 4 Do, and if so, how do these structural holes indicate state capture in Serbia?

The second step involves definition of sub-themes/categories. Therefore, to facilitate the content analysis of data, along with these themes, additional sub-themes were formulated (explained in detail in the Table IV.1). Theory supports that the concept of sub-themes or categories answers

122 questions such as who, what, when, or where, namely describes the main content (Berg, 2001).The grouping of the collected data into sub-themes was performed on the basis of the primary data, theories on similar topic and European standards. This process involved initial/pilot division of data into themes, and careful consideration of the data to be grouped into specific sub-themes or categories that explain in detail or form a part of the concrete theme.

The existing knowledge of each topic, based on legislative principles, EU standards and available assessments of the specific field, were utilised to group data under a certain sub-theme. Authors suggest that subject to the study’s aim and quality of the collected data, it is possible to formulate sub-themes or categories at the highest level of abstraction sufficient for reporting on the results, or develop themes (Berg, 2001; Elo and Kyngäs 2007). Hence the sub-themes represent the smaller units of the concrete theme. Sub-categories are often referred to as the sub-themes (Graneheim and Lundman, 2004). Authors hold that themes and sub-themes need to be internally homogeneous and externally heterogeneous hence that grouped data should not fit into more than one category (Krippendorff, 2004). Given the use of a deductive approach in this study, the role of the introduction of themes and sub-themes was to link the interpretations with the existing theories in order to draw inferences.

The sub-themes developed for the purpose of this study, still represent a broad group of data, conceptualized as the key set of words/concepts elucidating the concrete theme. The following sub-themes were developed:

Sub-theme 1: Democratic transition; EU accession

Sub-theme 2: Balkan route and Serbia’s role; Drug trafficking related events Sub-theme 3: Institutional framework; legislative framework

Sub-theme 4: Spheres of state capture;Indications of state capture arising from criminal networks

The third step involves the abstraction of raw data and categorisation. A detailed categorisation used for content analysis is presented in the Table IV.2 below. Given the amount of information a participant is sharing in the form of numerous sentences, those phrases or key words that most fit the main criteria for inclusion were extracted into smaller parts i.e. the meaning units.

123 Likewise, the written material used also entails a number of paragraphs relevant for the study, whereby certain key phrases and words needed to be extracted and the choice of content made.

In selecting the content that will be used as the “meaning units” and subsequent categories, the key guiding principles were the aim of the study and the research questions. Hence the choice of content was clearly defined and justified by the aims of the study and the research questions, throughout the whole analysis.

Analysis of the collected data was performed by dividing it into smaller units in the following manner. Meaning units represent the smallest units, obtained during data collection, which entail information necessary for the research. These meaning units are presented in the form of sentences, phrases or paragraphs. They originate from the transcribed interviews and the written material collected.

Identification of the meaning units involved an assessment whether all aspects of the content are included in relation to the aim of the study and the research questions. The data from the written sources, identified as meaning units relevant for the study, were marked by coloured pencils in different colours in order to distinguish the most important information from the less significant.

Once the main meaning units were included, the process of re-reading was performed to check whether additional paragraphs or phrases should be included. For the information obtained through the interviews, the key points of the discussion were marked, and some characteristic sentences were used to define the meaning units. The following extracts represent examples of how the raw data was then broken down into key themes:

"The Skaljarski klan is a Montenegrin criminal group which is in conflict with the Kavacki klan, but they are only parts of larger criminal groups that operate in both Serbia and the territory of the former Yugoslavia.

(P2.9)

Key theme: Organised crime networks

"The profit earned by smuggling cocaine is often placed in legal business by criminals, by paying politicians, directors of public companies and other officials. For example, they buy failed businesses, build buildings and business complexes. In this way, they damage the financial system in Serbia” (P2.7)

Key theme: State capture

"The dominance of the black market of narcotics is still the main reason for the criminals' war” (P2.4)

124 Key theme: Organised crime networks

“Balkan criminal groups are linked to international narcotics cartels".

Key theme: Organised crime networks

“It is very frustrating for me to judge in such circumstances...the members of the court panel oppose any decision to convict this person despite strong evidence and testimonies... it is like they perceive that the court should act to the benefit of the defendant even for highly positioned traffickers who are accused...” (P2.1)

Key theme: Effectiveness of state response network addressing organised crime

“Only the political elites here have achieved the right to autonomy and independence. Nobody else did.

Governing outside from the institutions has become practice in Serbia, from the 90s until today. And when you have extra-institutional power, it is usually performed with the help of the secret services” (P1.18).

Key theme: State capture

“Our judiciary is very bad, as a consequence of decades of abuse of democracy by the political elites. The judicial reforms resulted solely on replacements of judges on the basis of political criteria, personal revenge or the criteria of closed social groups. The level of corruption remained the same but the number of obedient and incompetent judges increased” (P2.10).

Key theme: State capture

“Accession is currently on hold...it has a set of issues surrounding it, such as migration, terrorism, euro scepticism, relations with Russia... but it seems Brussels is unconsciously hindering the process... the process is there, but there is no political will...” (P1.26)

Key theme: Stalled transition and EU accession

“I think Serbia will never enter the EU... The fact is that the EU does not want to accept the Western Balkan states, because the EU is not anymore what it used to be or what it could have been” (P1. 11)

Key theme: Stalled transition and EU accession

These meaning units were further condensed to form shorter phrases, without losing the sense of the content, to facilitate the analysis. Summarized or condensed meaning units were developed by using the key words from the meaning units that are mostly associated with the aim of the study and the research questions i.e. with different focus of the study. Determination of the summarized meaning units implies reduction of the number of words without losing the content of the unit (Graneheim and Lundman, 2004). This process facilitates further grouping of data

125 and subsequently the analysis. For instance, war between criminal groups; member of a group killed; Balkan criminal groups; implementation is lacking; not in our jurisdiction; sentenced below legal minimum; still no final judgement.

In order to perform latent content analysis, it was necessary to further minimize the number of words used and extract the logic of data i.e. group the data into specific units of analysis (similar terms used: content area Graneheim and Lundman, 2004; or domains Patton, 2002). The aim of the study and the research questions were used as the key guiding principle in defining the units of analysis. Each unit of analysis implies a different focus of the study. For example, trial postponed; amount of drugs found; car exploded; killed member of...; perpetrator known to police; no progress in the field; law track record on.

Finally, in order to perform the analysis, the final step was coding. The formulation of codes for each unit of analysis was performed. All the transcribed interviews and each written document or a part of the document was systematically examined, categorized as explained above and coded. Coding of all the data was performed by using specific key words from the condensed meaning units and/or unit of analysis, in order to closely associate the meanings and simplify the coding process. Such labelling of the data was referred to by Strauss and Corbin as conceptualising data by identifying and giving “each discrete incident, idea or event, a name … that stands for or represents a phenomenon” (Strauss and Corbin, 1990, p. 63).

Pre-testing of the coding scheme on several samples was performed. The coding list was developed with explanations of the codes to secure reliability (Morse and Richards, 2002). The coding of these samples was discussed with a colleague in order to check the level of consistency.

For example, CG (group of XY), D (drugs), R (route), MIN (ministry of..), IMPL (implementation), COO (cooperation), EUTR (track record in accession related field as measured by the European Commission).

Line-by-line coding was performed through careful examination of the data, taking into account, throughout the whole process, the key research questions and the themes. The following steps in the process involved pre-testing of the coding scheme and follow up (revision of coding scheme).

Subsequently, coding full sample was performed.

126 As discussed above, the phrases from the interviews were grouped under the themes to reflect the participants’ statements about the given questions. Inferences were drawn on the basis of sub-themes and units of analysis that were generated for the purpose of the analysis. The consistency of the coding employed was assessed in order to weigh the data set validity and reliability. The analysis involved an exploration of different dimensions of the collected data with an aim to uncover the patterns relevant for the study.

The implied meanings were analysed by the author in order to draw authentic conclusions. Given that the author has extensive knowledge of the topic, both through educational activities, as well as the working experience in state institutions with public officials, it was important to capture these implied meanings in order to obtain innovative comprehension of the topics.

To present the results, it was decided to discuss the conclusions under the themes, with reference to the sub-themes, to adequately address the research questions. In addition, this approach enables the reader to understand the interpretations. The conclusions arising from the results are supported in the discussion with available theories, standards in the field and citations from the interviews i.e. meaning units.

The results and conclusions under the themes and sub-themes were utilised to group the information and conduct social network analysis, described in detail in the Table IV.2 below. On the basis of this comprehensive set of data, the following matters were explored:

a) Symptoms of stalled democratic transition and EU accession of Serbia and their association with endurance of organised crime;

b) The scale of organised crime in Serbia, in particular with regard to drug trafficking networks. Data on mafia related murders, car explosions, current trials on drug trafficking and seizures of drugs were particularly taken into account;

c) The network of state institutions (state response network) designed to combat organised crime and drug trafficking was identified and investigated in detail; The effectiveness of the work of state institutions was addressed and this data was linked with loopholes identified in the perceptions/views of the participants and/or referred to in official reports;

d) The extent to which the state response network is distorted by signs/symptoms of state capture was analysed.

127 As described previously, content analysis was selected as an appropriate analysis method to enable interpretation of the findings and identify the key variables for the social network analysis.

Implementation of social network analysis is described below.