Operationalizing the dependent variable, and the reputational character of the negative media

In order to operationalize the agencies’ reputation management strategy, and the reputational character

of the identified negative media coverage, I will use a quantitative content analysis method developed by Busuioc and Rimkute (2018). It is an analysis which captures the reputational dimensions – technical, performative, legal-procedural, and moral - by counting the ‘mentions’ of keywords that are incidences of references to a specific reputational dimension (Busuioc & Rimkute 2018, p. 17). To be more particular,

the agency’s reputation management strategy of a year is captured by counting the number of keywords per reputational dimension in the agency’s annual report of that year. Similarly, the degree to which negative media coverage appeals to certain reputational dimensions of the agency in a year, is captured by counting the number of keywords per reputational dimension in all the identified negative EP questions of that year. While this method might not give the best reflection of the ‘nuance’ involved behind the

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communications, it offers the advantage of being systematic and replicable (Busuioc & Rimkute 2018, p. 17).

I chose to analyse annual reports because these documents provide a solid reflection of the

agency’s reputation management strategy. This is because annual reports are available for all audiences, ranging from the media, to citizens, and institutional actors, and are therefore documents that capture well how the agency wants to present itself to its various audiences (Busuioc & Rimkute, 2018, p. 13). Moreover, annual reports show what the agency prioritises, how it reconciles conflicting interest of various audiences, and show its attempt to project a unified image of itself (Busuioc & Rimkute 2018, p. 14). Since I will analyse the period 2012-2016, I will analyse the annual reports of both agencies from the years 2012 up until 2016. More specifically, I will analyse the agency’s general annual reports, but if not available for a certain year, I will use that year’s annual activity report as a substitute. This provides me with a good overview of EASO’s and FRONTEX’s reputation management strategy over time and allows me to generate comparable data across these two agencies.

But with what keywords are Busuoic and Rimkute (2018) capturing the technical, performative, procedural-legal, and performative dimension? With the technical dimension they include words referring to scientific or technical vocabulary, or words with scientific connotations (Busuioc & Rimkute 2018, p. 15). This is different from the performative dimension with which they include words referring to the proactive or assertive role of agencies, words referring to attained ends, and words such as effective/efficient which signify the overall efficacy of the agency’s performance (Busuioc & Rimkute 2018, p. 15). The legal-procedural dimension includes words referring to organisational processes, procedures, formal processes, rules, and formal requirements (Busuioc & Rimkute 2018 p. 16). And lastly, the moral dimension includes words referring to the transparency, flexibility, trustworthiness, honesty credibility, integrity, and openness of the agency, and words aiming at protecting the consumers, users, and constituents (Busuioc & Rimkute 2018, p. 16).

Table 1 (page 52) gives an overview of these keywords. The ‘*’ behind a keyword denotes and allows the word to end differently. E.g. ‘analy*’ can stand for example for the word ‘analysis’, for its plural version ‘analyses’, but also for the verb ‘analyse’. The ‘_’ before, after, or in between keywords in the

overview denotes ‘a space’. Apart from adopting the keywords, I am also adopting their created

‘conditions’ for certain keywords. The ‘condition’ function allows the user to exclude keywords when

arising in specific combinations that are in these specific combinations not incidences of references to one of the four reputational dimensions (Busuioc & Rimkute 2018, p. 16-17).

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Though, while Busuioc and Rimkute (2018, p. 17) excluded all words that were sector specific in order to avoid biases in favour of particular fields, I will not exclude all these words. Contrary to Busuioc and Rimkute I am not researching regulatory agencies from all different sectors, but researching two agencies operating both in the AFSJ and CEAS. This allows me to include sector specific words that are applicable to both, or which to both agencies mean the same. Examples of these are words us ‘Human Right*’, and ‘Fundamental Right*’, which capture the moral dimension. Both EASO and FRONTEX are namely as JHA agencies subject to the EU Charter of Fundamental Rights (which also encompasses Human Rights) (European Parliament Directorate-General for Internal Policies, 2011). Other words that are added

because of this similar reason are: ‘humanity’, ‘human dignity’, ‘statistics’, ‘code of conduct’, and ‘guideline*’. These words can be found in Table 2.

However, even though both agencies are operating within the AFSJ and CEAS, they do not fulfil the same tasks. Therefore, one agency might use some words much more than the other agency, simply

because of the nature of its tasks. In order to avoid these biases, I have tried to avoid ‘agency specific words’. Examples includes words such as ‘secur*’, sav*, and ‘safe*’. While these keywords would capture

the moral dimension, it would bias the result in favour of FRONTEX as compared to EASO. The word

‘secur*’ would be unfair to include because FRONTEX deals with the Schengen dimension of CEAS, which

in practice means that it is provided with the task to secure the borders, while EASO tackles the unfair distribution of applications by asylum-seekers caused by CEAS. Hence, it is likely that the word ‘secur*’, would be much used by FRONTEX due to the nature tasks, and not by EASO. For the words ‘sav*’, and ‘safe*’ the similar reasoning applies. While FRONTEX directs is activities towards irregular immigrants who, amongst other, cross the Mediterranean Sea in precarious conditions, EASO directs its activities towards the irregular immigrants once they have already arrived safely. Hence, due to the nature of its tasks FRONTEX comes in the situation to save migrants or to provide them with safety, while EASO has to be significantly less concerned with that.

Another large difference between the framework used in this thesis, and the one used by Busuioc and Rimkute is that I use the key words not only for measuring the reputational dimensions in the

agencies’ annual reports, but also for measuring the extent to which the negatively labelled EP questions

appeal to the agencies’ reputational dimensions. Hence, I include more keywords (see Table 2 on page

53). Firstly, I include keywords that one would find less likely in annual reports, but more likely in a question format such as EP questions. For example, the word ‘step*’ is often used in the following context: “What steps are taken to ensure… etc.” With the word ‘step*’ the questioner appeals to the agency’s

51 Keyword categories Keywords

Technical reputation

reliab*, test*, analy*, assess*, calcul*, data, evidence*, examin*, expert*, investigat*, knowledge, likelihood*, methodology*, model*, profession*, qualitat*, quantify*, quantitat*, research, rigor* robust*, science*, scientif*, studi*, stud*, technic*.

Performative reputation

Deliver*, enforce*, result*, achieved, achievement*, action*, adopt*, _decision*, application*, assertive*, complianc*, comply, effecti*, efficien*, goal*, improv*, inspect*, KPI* [Key Performance Indicator], objectives, oblige*, outcome*, output*, performance*, restrict*, success*, timely*, target*.

Legal-procedural reputation Consult*, formal*, independen*, protocol*, process*, rule*, access_to_document*, access_to_information, appeal*, conflict*_of_interest*, control_standard*, control_system*, declaration*_of_interest*, internal_control*, internal_operation*, internal_system*, judicial*, legal*, liability,

management_standard*, management_system*, procedur*, provisions, requirement*.

Moral reputation

Protect*, values, committed_to, common_interest*, consumer*, credibility, dialogue*, engagement*, ethic*, flexibl*,

good_governance, honest*, inclusiv*, integrity*, moral, openness*, precaution*, public_interest*, respect_for, safeguard*, societal*, transpar*, trust*, users.

Table 3: WordStat Keywords of Busuioc & Rimkute (2018)

I included because of this reason are: ‘fact-finding’, ‘sophisticated tool’, ‘active role’, ‘do* to prevent’, ‘radical change’, ‘cost-effectiv*’, ‘cost-effici*’, ‘pro-activ*’, ‘in accord* with’, ‘principle* of’, ‘lawful*’, ‘in line with’, ‘conform*’to, and ‘conscience*’. Secondly, I include keywords that specify that the agency is NOT or insufficiently emphasizing, or acting upon, a certain dimension of its organizational reputation,

(with the possibility thatit might lead to ‘undesirable’ outcomes). One is of course also not likely to find

these types of words in annual reports. These are: ‘inability*’, ‘fail*’, ‘inaction’, ‘do more’, ‘shortcoming*’, ‘ineffecti*’, ‘inefficien*’, ‘unlawful*’, ‘breach* of’, ‘irregularit*’, ‘violation of’ ‘acting illegal*’, ‘illegal action’, unacceptable’, ‘savage’, ‘brutal*’, traged*, ‘tragic*’, ‘suffering’, ‘inhumanity’, ‘horror*’, ‘endanger*’, ‘murderous’, ‘humanitarian emergenc*’, and ‘inhuman’.

52 Keyword categories Keywords

Technical reputation Statistics, fact-finding, sophisticated_tool*.

Performative reputation step*, inability, fail*, inaction, do_more, shortcoming*, ineffecti*, inefficien*, active_role, do_to_prevent_, radical_change, cost-effectiv*, cost-effici*, pro-activ*.

Legal-procedural reputation in_accord*_with, principle*_of_, unlawful*, lawful*, breach*_of, in_line_with, conform*_to_, irregularit*, acting_illegal*, illegal_action, guideline*, code_of_conduct, violation_of.

Moral reputation

Human_right*, Fundamental_right*, Humanity, traged*, conscience*, brutal*, tragic*, shameful*, horror*, murderous, savage, inhumanity, endanger*, humanitarian_emergenc*, unacceptable, inhuman, human_dignity, suffering.

Table 4: Added Keywords

I will present the outcomes of both the agencies’ reputation management strategy, as well as the reputational character of the negative media coverage, in graphs. Though, I will deliberately present it in different types of graphs. While I represent the reputational dimensions conveyed by the annual reports as a percentage of the total amount of words in these reports, I represent the reputational dimensions conveyed by the identified negative media coverage in absolute numbers and/or as a percentage whereby the four reputational dimensions in the end up to a 100%. The reputational dimensions conveyed by the negative media coverage is represented in this manner because what matters in the end is the total number of reputation bearing keywords per year, not the number of keywords in comparison with the total number of words per year. This is opposite to the reputational dimension conveyed by the annual reports, whereby the actual number of keywords capturing a reputational dimension is of lesser importance than the relative weight of a reputational dimension within an annual report.