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Media and Bureaucratic Reputation: Exploring Media Biases in the Coverage of Public Agencies

  • Jan BoonEmail author
  • Heidi Houlberg Salomonsen
  • Koen Verhoest
  • Mette Østergaard Pedersen
Chapter
Part of the Executive Politics and Governance book series (EXPOLGOV)

Abstract

How agencies perceive, process, and prioritize multiple (potentially conflicting) audiences’ expectations of components of their reputations is a core interest of bureaucratic reputation theorists. Agencies must choose which dimension(s) to stress towards specific audiences, a process referred to as ‘prioritizing’. Boon, Salomonsen, Verhoest, and Pedersen challenge a central argument of contemporary bureaucratic reputation theory, namely that prioritizing assumes government agencies to be rational, politically conscious organizations with incentives to avoid reputational damages and political sanctions. The chapter tests the claim that agency behaviour is (at least to some extent) driven by the distinctive logic of the media rather than by assessments of the relative strength of different dimensions of an agency’s reputation that are subjected to threats, or by the nature of the agency’s task.

Keywords

Bureaucratic reputation Media coverage Prioritizing Velcro effect Mediatization Agencies 

Introduction

Different theoretical perspectives on organizational reputation have been developed in several research traditions (for an overview, see Wæraas & Maor, 2015). This chapter can be situated in the political science approach to bureaucratic reputation theory (BRT), characterized by its empirical focus on government agencies and their standing within political-administrative systems (Carpenter, 2010).

Within the BRT framework, a bureaucratic reputation is defined as ‘a set of symbolic beliefs about the unique or separable capacities , intentions, roles, obligations, history and mission of an organization that are embedded in a network of multiple audiences’ (Carpenter, 2010, p. 33). Today’s knowledge society and blame culture have increased organizational concerns with reputational risk. According to Daniel Carpenter (2004), reputation-cultivating and/or -protective behaviour is the most powerful dynamic governing organizational behaviour. BRT scholars are interested in how agencies identify and respond to threats to their reputation. A series of studies demonstrate how agencies respond to reputational threats by changing the timing of their decisions (Carpenter, 2002), the public observability of decisions (Maor, 2011; Moffitt, 2010), their outputs (Maor & Sulitzeanu-Kenan, 2016), or their strategic communication (Gilad, Maor, & Ben-Nun Bloom, 2015; Maor, Gilad, & Ben-Nun Bloom, 2013).

Carpenter (2010) has proposed a framework of four dimensions of an agency’s reputation: (1) performative, the ability to execute its tasks effectively, with respect to both outcome and output; (2) moral, the ability to meet the normative expectations posed to public organizations, such as protecting citizens and ensuring transparency; (3) technical, which depends on the ‘expertise’ and professional qualifications of the organization; and (4) procedural, which refers to the extent that the organization conforms with set procedures and legislation (pp. 44–46). How agencies perceive, process, and prioritize multiple (and potentially conflicting) audiences’ expectations of different components of their reputation is a core interest of reputation theorists. Agencies must choose which dimension to stress towards specific audiences. This process is referred to in the literature as ‘prioritizing’ (Maor, 2015, p. 32). The act of prioritizing assumes that government agencies are rational and politically conscious organizations with incentives to avoid reputational damages and ultimately political sanctions.

In this chapter, we challenge the alleged rationality that is often assumed to be part of the prioritization process. More specifically, we test the claim that agency behaviour is (at least to some extent) driven by the distinctive logic of the media rather than merely by assessments of the relative strength of different dimensions of their reputation that are subjected to threats, or by the nature of their task .

Before delving further into this claim, it is important to note that a focus on the media is not new in the BRT field. In general, the notion that agencies are open systems whose behaviour is explained through relations with their broader environment, rather than merely via their relation with political superiors, has been at the core of reputation-based studies (Maor, 2015). The media serve as crucial players in the reputational calculus of reputation-sensitive agencies. First, the media are rapidly gaining power as informal forums for political accountability (Bovens, 2007). They are the most important source of information about government performance for citizens (Arnold, 2004). Second, the media serve a dual role as audiences in their own right and as institutional intermediaries used by other audiences—and the agencies themselves—to make sense of agency performance. Therefore, BRT scholars recognize that for many agencies, the media will be an important channel through which they signal their reputation uniqueness to their manifold audiences.

However, we argue that most BRT studies do not take into account that the media work according to a logic of their own (Altheide, 2004), which might interfere with the reputation signals agencies seek to send. Existing scholarship tends to treat the role of the media as a black box. Studies have insufficiently looked at the independent effect of media reporting as a biased representation of reality, which is shaped by media-specific criteria of relevance. This chapter contributes to the field by theoretically developing and empirically testing biases in media reporting about agencies. The priorities of the media should be part of the equation when accounting for agencies’ behaviour. The media may have their own biases in terms of their reporting, which in turn may affect what are considered to be important reputational dimensions, audiences, and so on for agencies to cultivate. More specifically, this chapter examines media biases in terms of negativity and the media-induced Velcro effect, related to the number of reputational dimensions that are reported about. This Velcro effect entails that organizations which faced intense negative media coverage in the past have much higher chances of being criticized on multiple aspects by negative media coverage and reputational threats by audiences in the future. In addition, we analyse whether agencies show a differential response to media reporting depending on newspaper article characteristics.

We find inspiration in the developing field of research on the mediatization of politics and bureaucracy (Fredriksson, Schillemans, & Pallas, 2015; Mazzoleni & Schulz, 1999; Strömbäck & Esser, 2014) and in the established literatures on news values (Galtung & Ruge, 1965; Van Aelst, Sheafer, & Stanyer, 2012) and agenda-setting (McCombs, 2014). The former field has illuminated that the media have established themselves as an important institution that increasingly influences other socio-political spheres, including government bureaucracies . The mediatization literature accentuates the general and increasing importance of the media as a force to which public agencies need to adapt and/or respond to in a strategic manner. The latter literatures provide insights into the criteria used by media outlets to determine whether and how to cover a story, and how definitions of issues set in the media influence the public and political agenda. These insights will be used to formulate some expectations for the news coverage of public agencies.

In connecting with literatures on the role of the media and its logic on different spheres in society, this chapter also makes a broader contribution to expanding the knowledge base of the nexus between media and the bureaucratic sphere. Since the turn of the century, the mediatization of politics has attracted significant scholarly attention (Mazzoleni & Schulz, 1999; Strömbäck & Esser, 2014). Because the media have developed into the most important source of information for people in advanced democracies, they hold the key to the public sphere. Virtually no political actor or institution can afford to ignore the media’s role in contemporary politics (Strömbäck & Esser, 2014). Public agencies have long been neglected in mediatization research. Recent studies, however, show that agencies have tended to internalize the media logic in their everyday practices (Fredriksson, Schillemans, & Pallas, 2015; Thorbjørnsrud, 2015). The nexus between reputation and media has gained broader academic and societal relevance as public values, routines, and organizational work procedures are increasingly adapted to the demands of the media.

The following sections discuss prioritization as a theoretically central, though empirically underdeveloped, concept of BRT. Hereafter, we bring in the media and discuss prioritization against the backdrop of mediatization.

Prioritization as a Central Idea of Bureaucratic Reputation Theory

Public agencies operate in uncertain contexts characterized by dilemmas, trade-offs, and paradoxes (Pollitt & Bouckaert, 2011; Verhoest, Verschuere, & Bouckaert, 2007). Many of these dilemmas consist of opposing value systems that ultimately reflect broader societal debates about what we expect from governments and the organizations within them. Agencies face a number of contradictory values in their daily work. For instance, values related to achieving political ends may conflict with values such as equality, fairness , and justice. Likewise, values related to morality may conflict with a strong market orientation (Goodsell, 1989; Wæraas & Byrkjeflot, 2012).

When cultivating their reputation, public managers must prioritize between potentially conflicting expectations. The core challenge that agencies face is that audiences have different (and potentially conflicting) expectations about the way the agency should exercise its tasks, which might change over time (Maor, 2016). As noted by Carpenter and Krause (2012), ‘satisfying some audience subset often means upsetting others or projecting ambiguity’ (p. 29). Furthermore, the effect of enhancing one reputational dimension implies that another dimension will likely suffer. Agencies, then, must choose which dimension to stress towards specific audiences, a process that is referred to as ‘prioritizing’ (Maor, 2015, p. 32).

The act of prioritizing assumes that government agencies are rational and politically conscious agencies with incentives to avoid reputational damages. In the words of leading BRT scholar Moshe Maor (2015):

… reputation-sensitive agencies are adaptive, strategic, and sometimes even opportunistic actors…. They have a repository of ideas, values, and strategies that they may combine in various ways, deploy them politically, and redeploy them between different audiences, thereby redefining relations with these audiences…. Specifically, reputation-sensitive agencies are able to adapt to in order to cope with criticism by external audiences—that is, to accommodate themselves to the preferences of their external audiences. They are able to manipulate external audiences’ opinions and shape rather than simply accommodate external audiences’ opinions, turning them into a component of agency behavior. (p. 25)

Prioritization can occur at several levels. First, prioritization exists at the level of reputational dimensions. To illustrate, Carpenter, Chattopadhyay, Moffitt, and Nall (2012) have examined whether administrative deadlines impact the Food and Drug Administration’s (FDA) timing of decisions. They focus on the trade-off between the FDA’s interest in producing accurate decisions and protecting its long-term reputation for expertise, on the one hand, and its interest in sufficient staff funding and short-term reputation for prompt action , on the other. Their results show that deadlines result in the piling of drug approvals right before the deadline, compromising the agency’s long-term reputation and, ultimately, the safety of approved pharmaceuticals. Second, prioritization exists between areas of agencies’ functioning. Agencies often perform different types of functions that might be prioritized in reputation management. Maor, Gilad, and Ben-Nun Bloom (2013) have demonstrated this for the case of the Israeli banking regulator, which performs tasks in the area of prudential regulation (where it enjoys a strong reputation) and in the area of consumer protection and competition (where it has a weak or evolving reputation). They show how this agency tends to remain silent on issues regarding which it generally enjoys a strong reputation and on issues that lie outside its distinct jurisdiction, while responding to opinions about core functional areas for which its reputation is weaker and areas wherein its reputation is still evolving. Third, prioritization exists at the level of stakeholder audiences. A reputational perspective suggests that not all audiences are equal in the eyes of reputation-sensitive agencies. For example, Busuioc and Lodge (2017) have argued that reputational concerns determine which audiences are prioritized by agencies and which competencies will be emphasized in the account-giving process.

What these different perspectives on prioritization have in common is an underlying assumption of a rational process during which politically conscious agencies strategically weigh the pros and cons of dedicating their limited time, energy, and resources towards specific audiences. Bureaucratic reputation theorists have so far primarily, and rather implicitly, suggested that prioritization is a precondition for an agency’s ability to cultivate a favourable reputation which ultimately leads to autonomy in the relation with the political principal (Maggetti & Verhoest, 2014; Verhoest, Rommel, & Boon, 2015). However, we take the position that agencies are not only political actors able to read, adapt to, and even manipulate their environments but also open systems that are affected by their environments. In the subsequent part of the chapter, we provide some empirical insights into how media biases relate to how agencies appear in, and respond to, reputational threats (and praises) in the media .

How the Media Might Affect Prioritization

This chapter seeks to contribute to the BRT literature through a critical reflection on the central idea of prioritization, the underlying mechanisms of which are poorly understood in empirical terms. We do not claim to come up with any final answers. Yet through some empirical illustrations, we hope to commence a discussion for further theorizing on the basis of rationality that underpins the prioritization process of agencies. The overall question, we feel, that BRT studies need to reflect more on refers to the extent to which prioritization is a rational–intentional calculus of the relative strength of their reputations and the task subject for reputational threats. In this chapter, we begin with a very focused theoretical and empirical contribution on the role of the media in the prioritization process of agencies. The media as an institution works according to a logic of its own, which has been shown by mediatization scholars to increasingly affect other spheres of society (Hjarvard, 2013), including the public sector (Pallas, Fredriksson, & Wedlin, 2016; Schillemans, 2012).

Hence, we explore the idea of the news media as an institution in its own right with a potential to influence how agencies appear in, and respond to, coverage about their actions, building on the concept of news values and agenda-setting . First, news values are general guidelines or criteria used by media outlets, such as newspapers or broadcast media, to determine how much prominence to give to a story (Galtung & Ruge, 1965). The media work according to a distinct set of professional norms and values is emphasizing more or less universally accepted criteria for newsworthiness such as timeliness, proximity, surprise, negativity, elite involvement, conflict, and personalization (Strömbäck & Esser, 2014; Van Aelst et al., 2012).

Second, a vast agenda-setting literature has shown the news media’s ability to influence the salience of (attributes of) issues on the public agenda (Carroll & McCombs, 2003; Deephouse, 2000; McCombs, 2014). News sources provide definitions of issues, and they frame problems in particular ways, thus providing the terms of future public discussion. This observation has obvious implications for agencies . As a result of news values and agenda-setting tendencies, it is important to stress that neither the image that is portrayed of agencies in the media, nor whether this image is picked up by the media and eventually ends up on the radar of political principals, are fully the result of agency strategizing. They are equally as much the result of journalists’ and editors’ decisions. This has led scholars to suggest the importance of an organization’s ‘media reputation’, defined as the overall evaluation of an organization as presented in the media (Deephouse, 2000, p. 1091). On the one hand, BRT studies have been blind to the role of the journalist, treating the media as a black box. On the other hand, agencies themselves need to be conscious of the role of the media in the construction of their media reputation .

Following the media’s inclination to report more about events or (aspects of) organizations that are more negative, more proximate, more conflictual, and involving elites, we formulate the following tentative hypotheses:
  • First, we expect that the media will pay relatively more attention to reputational threats than reputational praises (H1), following the media’s inclination to have a bias towards negative stories involving conflict .

  • Second, we expect that the media will pay relatively more attention to organizations’ performative reputations (H2), following the media’s bias towards stories that have a more direct impact on their readers.

  • Third, an agency’s reputation is formed because of audiences’ perception of an agency over time, meaning that organizations develop a ‘relational history’ with their audiences, formed by audience’s past judgement of its performance and prior assessments of its reputation (Coombs & Holladay, 2001). Such relational histories form ‘reputational histories’, which matters as they serve as Velcro increasing the likelihood of exposure to future reputational threats (Coombs & Holladay, 2001). This Velcro effect, introduced by crisis communication scholars, suggests that a negative reputational history primes audiences to be more negative towards an organization facing a new crisis (Coombs & Holladay, 2001), as audiences are more likely to remember negative rather than positive past behaviour of the agency (Coombs & Holladay, 2006). As noted by Coombs and Holladay (2001), a negative reputational history ‘attracts and snags additional reputational damage’ (p. 335). This follows the idea that once a negative frame about the organization is in place in the media, it leads to a media Velcro effect across reputational dimensions. More aspects of the organization are placed under scrutiny by the media in their pursuit of more negative reporting on an agency that is already subject to negative media coverage. Hence, we expect that the media will scrutinize more reputational dimensions of organizations with a negative reputational history (H3).

Next, this chapter focuses on whether the media’s biased attention is reflected in the organizations’ communication responses to reputational threats in the media. BRT scholars have examined these communication strategies in some detail (Gilad, Maor, & Ben-Nun Bloom, 2015; Maor, Gilad, & Ben-Nun Bloom, 2013). These studies emphasize communication as a strategic exercise, driven by a motivation to protect the organization from reputational threats, depending on reputational profile of different tasks .

This chapter explores communication responses from a different angle pertaining to the question: to what extent, and how, are article characteristics related to communication responses of agencies? For each article in which one of the agencies under study was criticized (reputational threat ) and/or praised (reputational praise), we coded whether the agency reacted to the threat or praise within the same article (see also Gilad, Maor, & Ben-Nun Bloom, 2015; Maor, Gilad, & Ben-Nun Bloom, 2013). In this chapter, we explore the role of article characteristics ‘page in the newspaper’ and ‘number of words’. As noted by Carroll and McCombs (2003, p. 37), journalists communicate the salience of a given story in different ways, including through the placement in the newspaper and the length of the article. Longer stories and stories closer to the front page indicate high salience . Agencies might consider media logic in their decision-making by anticipating that certain issue characteristics (such as conflict or personalization) will provoke longer stories and/or stories that are closer to the front page. In these cases, a rational agency response would be to include their viewpoint on the matter in the article in the form of a reaction.

We formulate the following two additional hypotheses:
  • We expect that longer articles will be more likely to contain a response from an agency to a threat or praise, compared to shorter articles (H4).

  • We expect that articles closer to the front page will be more likely to contain a response from an agency to a reputational threat or praise, compared to articles further away from the front page (H5).

Research Design and Methods

The data material consists of all news articles that mention one of the five most salient Danish agencies (Pollitt, Talbot, Caulfield, & Smullen, 2004; Verhoest, Van Thiel, Bouckaert, & Lægreid, 2012): the Danish Financial Supervisory Authority (Finanstilsynet), the Danish Health Authority (Sundhedsstyrelsen), the Danish Business Authority (Erhvervsstyrelsen), the Danish Customs and Tax Administration (SKAT), and the Danish Veterinary and Food Administration (Fødevarestyrelsen) in the Danish newspaper Berlingske Tidende in the time period 1 January 2006 to 31 December 2015. As the ambition of the empirical analysis is to take a first step towards illustrating the relevance of the hypotheses and the theoretical arguments, the choice of the agencies informing the hypotheses is crucial to ensure a high degree of salience in the media. The included agencies thus represent most likely cases.

To identify all relevant articles, we searched for the name of each agency in INFOMEDIA, a Danish database of all published articles in the newspaper. After excluding letters and non-relevant articles, 5960 articles were identified. The articles were then coded on the basis of whether they contain a reputational threat or reputational praise. A reputational threat was coded if the article contained an explicit threat that can be directly linked to the agency in some way, while a reputational praise was coded if the description of the agency, its action, or its non-action included positive framing. No threats or praises where coded if the agency or its actions were described in neutral terms.

For each threat or praise we then coded which reputational dimension was targeted and whether the article contained a response by the agency to the threat or praise. The coding was done on the basis of a codebook by trained student assistants. To assess inter-coder reliability, the coders were asked to code the same articles. Inter-coder reliability coefficients (Krippendorff’s alpha) were later calculated. The alphas for the variables were in general satisfactory (>0.7), but for two of the reputational dimensions, procedural and processual, the alphas were low: 0.45 and 0.17, respectively. This led to a systematic re-evaluation and quality check of all the coding by two full-time research assistants. If the two of them were in doubt about the coding, they discussed the coding to reach 100 per cent agreement on the coding.

We use Daniel Carpenter’s (2010, pp. 45–46) four-dimensional framework, and suggest the following operationalization of reputational dimensions:
  • The performative dimension addresses judgements on the quality, efficiency, and/or effectiveness of the services that are considered an agency’s outputs and outcomes. These services can be a set of activities or policy instruments, and also an initiative, programme, or a report, that is the final manifestation of the agency’s core task that is delivered to society, politicians, or other public actors.

  • The technical dimension refers to having the necessary resources in terms of capacities , expertise, and skills to perform its tasks. It is judged irrespective of the actual performance and is hence irrespective of any output or outcome.

  • The procedural dimension is reflected in explicit references to procedures, standards, norms and rules, which can be internal but also external to the organization, such as the constitution.

  • The moral dimension refers to an agency’s well-meaning intentions (regardless of actual output). Moral praises relate to an agency’s attentiveness and compassion to different client groups; honesty; integrity; fairness; ethical behaviour; openness and transparency; ability to prevent ‘inequity, bias, and abuse of office’; trustworthiness; and attentiveness to democratic values (e.g. transparency, equal rights, legal rights, responsiveness to citizens in terms of their being heard in processes and procedures, and equal access to service delivery for different groups). Moral threats relate to a lack of these qualities, but they also include references to turf protection, scandals, and/or indecent behaviour .

An additional dimension that is not covered by Carpenter’s categorization was defined to address some aspects of what agencies do. This dimension is defined as the processual dimension. Agencies perform different types of tasks broadly related to regulation, service delivery, or exercising other kinds of authority (Rolland & Roness, 2010). These tasks are composed of different processes, steps, and decisions which follow up on each other:
  • The processual dimension captures the processes and steps that do not directly reflect the final manifestation of the agency’s task delivered to society, politicians, or other public actors and/or the output or outcome of the organization, but which are merely serving as means (or preliminary steps) to realize those ends. For instance, the processual dimension involves a variety of internal processes and procedures such as the drafting of plans of action or internal reports, but also collaborations with external parties if there is no mentioning of outputs or outcomes.

If a reputational threat applied to more than one dimension (for instance, an agency that breaches formal rules about ethical behaviour), then each dimension to which the threat applied was coded (in the example here, both the procedural and the moral dimension). We constructed a variable that takes a value of 1 for multiple threats and 0 for single-dimension threats, for cases where several dimensions were threatened or praised in the article.

If an article contained an agency’s response to a reputational threat or praise, we coded this as a dummy where 1 means the article contains a response and 0 means the article does not contain a response. Non-responses might refer to agencies refusing to respond even though they were given the opportunity to, or to agencies not given the opportunity to respond by the journalist.

We operationalized negative reputational history as the percentage of articles with negative threat (s) in the last 365 days prior to the case article per organization. For the five agencies, this varies substantially in the time period from 2005 to 2015, from 0.0 per cent to 38.7 per cent, with an overall mean of 11.9 per cent. We also computed the same variable for positive reputational history as the percentage of articles with positive praises. As with the negative history, this varies from 0.0 per cent positive articles in the last 365 days to a high of 16.7 per cent articles for one agency in the last 365 days. The mean for positive reputational history is 4.7 per cent.

Empirical Illustrations

In this section, we present some empirical illustrations related to the hypotheses formulated earlier in the text. A total of 5960 articles were coded, 5243 of which were coded as neutral (88.7 per cent). Of the remainder, 717 were coded to contain a reputational threat (12 per cent), and 282 were coded to contain a reputational praise (4.7 per cent). The percentages do not sum to 100 because it is possible for articles to contain both threats and praises, which was the case for 79 articles in this sample.

Supporting H1 and mirroring the findings of previous scholarship (Deacon & Monk, 2001; Schillemans, 2012), we found that, although the majority of coverage involving agencies is neutral, the share of articles that contain a reputational threat strongly outweighs the share of articles that contain a reputational praise. The media, thus, is more likely to cover an agency’s mistakes than an agency’s accomplishments.

Table 9.1 presents an overview of the reputational dimensions that are addressed in the articles that contain a reputational threat and those that contain a reputational praise.
Table 9.1

Reputational dimensions

 

Performative

Technical

Moral

Procedural

Processual

Article with reputational threat

527 (73.5%)

164 (22.9%)

150 (20.9%)

93 (13.0%)

103 (14.4%)

Article with reputational praise

198 (70.2%)

75 (26.6%)

15 (5.3%)

5 (1.8%)

48 (17.0%)

Supporting the notion that the media has a bias towards covering stories that have a more direct impact on citizens (H2), we observe that the performative dimension (which includes assessments of the quality, effectiveness, or efficiency of an agency’s final outputs towards society) attracts the most attention, both in the negatively and in the positively toned articles. We see little distinction between articles with a reputational threat (73.5 per cent) and those with a reputational praise (70.2 per cent) in terms of the frequency of referencing the performative dimension. In both cases, the technical dimension is the second most covered category, which might have something to do with the fact that references about the quality of personnel and leadership, which relate to the news value of personalization, belong to the technical dimension. We do, however, see a distinction between the tone of coverage of the moral and procedural dimensions, both of which are more often covered negatively than positively.

Table 9.2 presents the results of the logistic regression models. In model 1, we regress whether the article contains a threat (1) or no threat (0) on the negative and positive reputational history of the agency. As a robustness check, we also ran logistics models where multiple threats (1) were compared with single threat (0), thus only utilizing articles with reputational threats. The models with reputational history cover the years between 2007 and 2015. The year 2006 was removed as we need at least one preceding year to calculate the reputational history. In all models, we have included dummies for years and organization which are, however, not shown in Table 9.2.
Table 9.2

Logistic regressions models

 

Model 1

Model 2

Multiple dimensional threat (1)

No threat (0)

Single threat (0)

Negative reputational history

7.629*** (6.912)

4.601** (3.068)

Positive reputational history

0.080 (0.026)

−2.998 (−0.637)

Year dummies

Included but not shown

Organization dummies

Included but not shown

Constant

−4.589*** (−7.003)

−0.764 (−0.890)

N

5346

601

Pseudo R2

0.084

0.094

Note: z statistics in parentheses. +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. Robust standard errors in parentheses

To illustrate and simplify the relationship between the variables, we show figures with the predicted probabilities of the independent variable of interest only if the coefficient is significant in the regression models. The predicted probabilities were done as average predictions (as observed) across the five agencies (Long & Freese, 2014). We also show the distribution of the examined independent variable in the predicted probability figures (see Figs. 9.1 and 9.2).
Fig. 9.1

Predicted probability of multiple threats as function of negative reputational history, based on model 1 (multiple threats (1) vs. no threats (0))

Fig. 9.2

Predicted probability of multiple threats as function of negative reputational history (only articles with threats), based on model 2 (multiple threats (1) vs. single threats (0))

In support of H3, the different analyses indicate the same trend: as negative reputational history increases, so does the likelihood that an article includes a threat on multiple dimensions. As hypothesized, we observe a Velcro effect across the dimensions that are reported in the media reports. Intense negative coverage leads to an increase in the possibility that multiple dimensions of an agency’s functioning will be criticized at a later stage. This finding is in line with recent theorizing on crises as being dynamic, that is, potentially changing in subject during the course of the crisis (Frandsen & Johansen, 2017, pp. 47–48). Note that we do not observe the same pattern for positive reputational history, which has no significant effect on the presence of multiple dimensional threats.

Finally, Table 9.3 gives insight into the relation between article characteristics (length and page) and agencies’ likelihood to respond within articles that contain a reputational threat . Figure 9.3 depicts this relation graphically.
Table 9.3

Relationship between article characteristics and response likelihood

 

Model 1

Model 2

Model 3

Length (log)

0.438** (2.871)

 

0.462** (2.973)

Page number (log)

 

−0.062 (−0.518)

−0.126 (−0.976)

Year dummies

Included but not shown

Organization dummies

Included but not shown

Constant

−2.903** (−2.822)

−0.051 (−0.116)

−2.795** (−2.680)

N

647

647

647

Pseudo R2

0.066

0.055

0.067

Note: z statistics in parentheses. +p < 0.10, *p < 0.05, **p < 0.01, ***p < 0.001. Robust standard errors in parentheses

Fig. 9.3

Predicted probability of response as function of article length (only articles with threats)

The analyses find support for H4. The coefficient for article length is statistically significant and positive, thus indicating that articles are more likely to contain an agency’s response to a reputational threat or praise as the length of the article increases. Figure 9.3 further illustrates this positive relation between article length and likelihood to contain a response. Most articles are distributed around 400–1100 words (between log(6) and log(7)). For an article of around 400 words, the probability that it contains an agency response is predicted to be 22 per cent. That probability increases to 30 per cent for an article of around 1100 words (different at p < 0.01).

Finally, the coefficient for the page number is negative, suggesting that articles are more likely to contain an agency’s response as they appear closer to the first page. This finding offers preliminary support for H5, yet the observed relationship is not statistically significant.

Discussion and Conclusion

This chapter explored the mediatized context in which bureaucratic reputations are managed. We present a first explorative analysis on the extent to which newspaper coverage on public agencies is shaped by media-specific criteria of news values. Our analyses suggest the presence of a media logic to which reputation-conscious agencies respond. News articles are more likely to contain negative than positive stories (though the vast majority is neutral). Also, the media are more likely to report stories related to the performative dimension of reputation, which are more proximate to readers. We further find that more intense negative coverage is related to an increase in the probability that more dimensions of reputation will be criticized at a later point in time (suggesting a potential Velcro effect across the dimensions). Finally, articles are more likely to contain a response from the agency as their length (number of words) increases.

These findings have practical implications. Our results suggest that the media is more likely to cover an agency’s mistakes than its accomplishments. Furthermore, agencies that enter into periods of more intense media coverage are more likely to be criticized on multiple aspects of their functioning. A clear implication for public agencies is to take the potential negative effects of media coverage seriously and to pay close attention to their media reputation. Studies on the mediatization of bureaucracy consistently show how (at least some) agencies increasingly anticipate and adopt news logic in their daily work (Fredriksson, Schillemans, & Pallas, 2015; Thorbjørnsrud, 2015). Anticipating negative coverage in a media setting that thrives on conflict, drama, and negativity is crucial for reputation-conscious agencies.

Our results also have implications for future studies that examine organizational reputation in the media. First, our findings can be interpreted as support for the benefits of a neutral reputation (Luoma-aho, 2007). Excellent reputations are risky in a public sector context. The better an agency’s reputation, the higher its fall will be in case of a crisis. For public sector organizations, this risk may often be too high to take, as their functions require stakeholders’ trust no matter the situation (Luoma-aho, 2007, p. 6). We find that media coverage is more often negative than positive (showing the risk that media attention carries), but that the overall majority of coverage is neutral. The mechanisms, however, remain unclear. Is neutral coverage the result of the media’s tendency to report about agencies in a neutral manner (and if so, how does this relate to agencies’ reputation management strategies )? Or is neutral coverage to some extent a reflection of agencies’ own desire to maintain a neutral media profile? Either way, given the potential risks involved in cultivating an excellent reputation, one might also ask whether agencies can run the risks involved in having a neutral reputation in times when politicians are more than willing to regain political control, strengthen managerial accountability, and/or reduce agency autonomy if an agency faces reputational threats. We take the position that agencies are not only political actors able to read, adapt to, and even manipulate their environments, but also open systems that are affected by their environments.

Second, at the very least, we urge BRT scholars to no longer treat the media as a black box, but to recognize the media as an institution to which other institutions, including agencies, must respond. Current studies run the risk of overestimating the strategic nature of reputation management or the effects of these supposed strategies . Public organizations are not only biased in their attention themselves but also are facing attention biases among relevant stakeholders. This chapter focused on attention biases in the media’s coverage of agencies as a result of news criteria (e.g. negativity, conflict and personalization). We urge future research to unravel the extent and conditions under which agencies are able to cultivate their reputation in a strategic manner through the media.

A main limitation of this chapter is its descriptive and decontextualized approach. Our aim is not to offer a concluding answer to the question of how media biases relate to reputation management. Rather, we sought to open up a discussion on how the distinct rationale of the media might provide fresh insights into a field that is dominated by agency-centred explanations for bureaucratic behaviour. We have been careful in stating that our results suggest certain effects that might be explained by media biases. Yet alternative explanations are also possible. Is the emphasis on the performative dimension of organizational reputations the result of media bias, or of strategic considerations of the agency that stress the performative dimension towards the media? Is the decreasing likelihood of including agency responses in shorter articles the result of the journalist’s space restrictions, or are there other factors at play? Does the so-called Velcro effect we observed indeed signal a bias in audience and media attention, or are we by coincidence just witnessing periods of real-life malfunctioning across the reputational dimensions of the agencies included in this study? In order to explain the nexus between media and reputation management, we call upon future studies to look further into the mechanisms that underlie the relations we have observed in this chapter.

Notes

Acknowledgements

We thank Stefan Boye for his help with the statistical analyses for this chapter. We also thank Martin Moos for collecting the data. The research is part of the Rep Gov project, funded by the Danish Reserach Council for Independent Reserach.

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Copyright information

© The Author(s) 2019

Authors and Affiliations

  • Jan Boon
    • 1
    Email author
  • Heidi Houlberg Salomonsen
    • 2
  • Koen Verhoest
    • 1
  • Mette Østergaard Pedersen
    • 2
  1. 1.Department of Political ScienceUniversity of AntwerpAntwerpBelgium
  2. 2.Department of ManagementAarhus UniversityAarhusDenmark

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