From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions

Vogel, Rick; Vogel, Dominik; Liegat, Marlen Christin; Hensel, David Article — Published Version From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions Public Administration Review Provided in Cooperation with: John Wiley & Sons Suggested Citation: Vogel, Rick; Vogel, Dominik; Liegat, Marlen Christin; Hensel, David (2024) : From social categorization to implicit citizenship theories: Advancing the socio‐cognitive foundations of state–citizen interactions, Public Administration Review, ISSN 1540-6210, Wiley Periodicals, Inc., Hoboken, USA, Vol. 85, Iss. 2, pp. 402-418, https://doi.org/10.1111/puar.13844 This Version is available at: https://hdl.handle.net/10419/319325 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. 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If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. http://creativecommons.org/licenses/by-nc/4.0/ RESEARCH ARTICLE From social categorization to implicit citizenship theories: Advancing the socio-cognitive foundations of state–citizen interactions Rick Vogel | Dominik Vogel | Marlen Christin Liegat | David Hensel Department of Socioeconomics, Universität Hamburg, Hamburg, Germany Correspondence Rick Vogel, Department of Socioeconomics, Universität Hamburg, Von-Melle-Park 9, Hamburg 20146, Germany. Email: [email protected] Funding information Deutsche Forschungsgemeinschaft, Grant/Award Number: 438530488 Abstract Public administration research has recently paid increasing attention to public employees’social categorization of citizens and the consequences thereof for administrative decision-making. We advance this line of scholarship by theorizing the concept of implicit citizenship theories (ICTs) and elaborating it in four sequential empirical studies. ICTs are implicit assumptions about citizens’typical or ideal characteristics that emerge freely and associatively in the minds of public employees and guide cognitive categorization. We investigate the content and structure of these ICTs and extract a taxonomy of six distinctive, yet interrelated, prototypes of citizens that public employees bring to their jobs. As implicit theories influence attitudes and behaviors, ICTs are relevant to the quality of public services delivered to citizens. This is particularly evident where negative prototypes give rise to unequal treatment. Practical interventions to change those prototypes might target their individual beholders or the social context that makes ICTs accessible. Practitioner points •Public employees bring cognitive schemas about ideal/typical citizens to their work, organized in a taxonomy of six prototypes of citizens and building implicit, everyday theories about citizens. •Public managers should be aware of employees’implicit citizenship theories because they might influence behaviors and decisions and, in turn, unintendedly violate the norms of equity and neutrality. •If negative prototypes of citizens prevail, public managers might “unfreeze” them by exposing employees to contrasting signals, reducing cognitive load, or inducing goals to think of citizens in counter-prototypical ways. •These interventions are likely to be more effective if implemented repeatedly and immediately before interactions with citizens. •However, changing implicit citizenship theories might be most effective when managers alter the public service ecosystem, as the accessibility of implicit theories is contingent on contextual cues from the social environment. INTRODUCTION "Conceptions of the job imply conceptions of the clientele. One cannot practice without an implicit model of the people on whom one is practicing" (Lipsky, 2010, p. 152). Scholarship in public administration (PA) has recently paid increasing attention to public employees’social categorization of citizens. Related research in street-level bureaucracy (e.g., Bisgaard & Pedersen, 2022; Raaphorst & Groeneveld, 2018) and representative bureaucracy (e.g., Bradbury & Kellough, 2007; Gade & Wilkins, 2013) Received: 29 June 2023 Revised: 8 April 2024 Accepted: 8 May 2024 DOI: 10.1111/puar.13844 This is an open access article under the terms of the Creative Commons Attribution-NonCommercial License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. © 2024 The Author(s). Public Administration Review published by Wiley Periodicals LLC on behalf of American Society for Public Administration. 402 Public Admin Rev. 2025;85:402–418. wileyonlinelibrary.com/journal/puar shows that employees’attitudes and decisions may differ substantially depending on what characteristics and behaviors citizens exhibit in interactions with the state. Some studies have examined how public employees respond to particular stimuli sent by citizens and drawn inferences pertaining to the salience of specific categories, most frequently gender (Bisgaard & Pedersen, 2022) and ethnic stereotypes (Petersen, 2021; Ratzmann, 2021). Other studies have focused on similarly narrow categories that are predefined upon theoretical considerations or implemented in bureaucratic procedures (Moynihan et al., 2022), such as the deservingness of citizens (Jilke & Tummers, 2018; Ratzmann, 2021; Senghaas, 2021). No prior study, however, has theorized and explored the broader taxonomy of generalized social categories of citizens that emerge freely in the minds of public employees. This is a missing piece in the puzzle of social cognition in state–citizen interactions because the cognitive filters through which public employees perceive and respond to citizens are likely to have a profound impact on their job-related attitudes, behaviors, and decisions (Allport, 1954; Fiske & Taylor, 2021). What public employees think about citizens is relevant not only from a scholarly point of view but also in practical terms. A key lesson from the field of service management is that the delivery of public services interferes with the whole-life experience of service producers and users (Osborne et al., 2022). Part of the experience that both sides bring to the service process is their assumptions about each other, often held at subconscious levels. On the part of frontline employees, such “implicit customer theories”(Hommelhoff, 2017) foster the perception of users as customers of a particular category, with considerable impact on the process and outcomes of service delivery (Sutikno et al., 2023). Public managers who are in charge of the operational quality of public services and the overall service strategy are challenged to act when employees’images of citizens are negatively charged. Such attitudes may be reciprocated by the service users who, in turn, may withdraw from the co-production of the service. Once this vicious cycle is activated, it is likely to result in a decline of service quality at the expense of public value (Osborne et al., 2022). These consequences are of obvious political and practical concern where negative images of citizens give rise to discriminant behaviors that undermine fair and impartial treatment, often with severe consequences for citizens’professional and private lives (e.g., Senghaas, 2021). Recognizing employees’ implicit theories about citizens may help public managers to respond to these adverse conditions of service delivery, for example, in the selection of personnel, in communicative interventions, or in the design of trainings. The content and structure of the social categories that public employees hold about citizens and bring to their jobs thus deserve more attention in PA scholarship and practice, given the field’s enduring interest in state– citizen interactions (Guy, 2021; Jakobsen et al., 2019). We address this gap both theoretically and empirically. In theoretical terms, we integrate and advance previous research on public employees’social categorization of citizens by further developing the notion of “implicit citizenship theories”(ICTs) (Liegat et al., 2022). By ICTs, we mean people’s everyday or lay theories about typical or ideal characteristics of citizens. We argue that ICTs serve as generalized interpretive frames through which public employees process experiential cues from specific interactions with citizens, providing public employees with social categories (i.e., prototypes) for heuristic judgments and decision making. In empirical terms, we conduct a series of qualitative and quantitative, exploratory, and confirmatory studies to extract the content and structure of ICTs in independent samples of public employees in Germany. We apply a rapid response measure (RRM) (Asseburg et al., 2020; Meade et al., 2020), which allows us to account for the “implicit”in ICTs. This is helpful because responses under speeded conditions are more likely to reflect the tacit dimension of what public employees think of citizens than self-reports in traditional questionnaire designs. Results suggest that public employees indeed have patterned associations with citizens, reflecting their a priori theories about citizens. Our findings confirm six interrelated, yet distinct, prototypes (i.e., Engagement, Kindness,Determination,Reticence,Neediness, and Foreignness) that constitute the overarching structure of ICTs. With this taxonomy, we improve the understanding of state–citizen interactions and their cognitive underpinnings. Given the importance of such interactions for outcomes on both sides of the relationship (Jakobsen et al., 2019), our study contributes to the various literatures that are concerned with state–citizen interactions, including precursors of a social cognition approach in recent PA scholarship (Liegat et al., 2022; Raaphorst & van de Walle, 2018). Practitioners learn from the results that public employees bring preexisting assumptions about citizens to their jobs that might be unrecognized in everyday work. ICTs deserve practical attention because they might translate into intentions and behaviors, thus having an impact on the treatment of citizens and the quality of delivered services. We discuss interventions to “unfreeze”prototypes, targeting both individual beholders and the social context in which ICTs emerge. THEORY Social categorization in state–citizen interactions Social categorization is the process by which people categorize themselves and others into distinct social groups (Turner et al., 1987). While not all previous studies on public employees’social categorization of citizens subscribe to social categorization theory and apply this PUBLIC ADMINISTRATION REVIEW 403 terminology, many researchers in this field would support the view that the way in which public employees encounter and respond to citizens may depend on how well the target person (i.e., the citizen) matches broader social categories. Scholarship in social cognition and beyond has demonstrated that categorization is a universal feature of human cognition and enables the processing of otherwise overwhelming amounts of information (Banaji & Greenwald, 2013; Fiske & Taylor, 2021). However, PA scholarship differs considerably in how, if at all, this perceptual and often tacit process of social categorization is reflected in empirical studies. Previous research has predominantly established direct links between particular stimuli emanating from individual citizens and the responses of public employees. In this behaviorist view, citizens send various kinds of signals to which public employees respond with differential attitudes, behaviors, and decisions. There are many studies on employee responses to socio-demographic stimuli, such as ethnic cues (Petersen, 2021) or membership in social classes (Raaphorst & Groeneveld, 2018). Some other research examines how stimuli emerging from citizens’ competencies and motivations, such as helping intentions (Guul et al., 2021) or administrative literacy (Döring & Jilke, 2022), trigger public employees’responses. Still other studies show that responses may be stimulated by citizens’behaviors, such as hard work (Jilke & Tummers, 2018). At this broad range of citizen characteristics and behaviors, stimuli may also interact when cooccurring in the same person (Bisgaard & Pedersen, 2022). PA scholars have also demonstrated that the context in which public encounters occur matters for the categorization. For instance, conditions of high workload (Andersen & Guul, 2019) and the region of residence (Grissom et al., 2009) can have an impact on how public employees respond to citizen stimuli. Whereas researchers have theorized cognitive processes to mediate the stimulus–response link (Jilke & Tummers, 2018; Raaphorst & Groeneveld, 2018; Raaphorst & van de Walle, 2018), they have only selectively and mostly indirectly examined the cognitive filters through which public employees perceive and respond to citizens. An example is research on stereotypes, which focuses on the processing of specific signals (such as gender or race) and draws inferences from employees’differential responses to these cues on the presence of underlying social categories that are charged with prejudices (Andersen & Guul, 2019; Bisgaard & Pedersen, 2022; Petersen, 2021). Other research places a similarly narrow focus on specific categories that emerge from bureaucratic procedures, such as the worthiness or deservingness of citizens (Jilke & Tummers, 2018; Ratzmann, 2021). However, scholarship is still particularly incomplete with regard to the a priori categories that feed into this process of social categorization. Since stimuli from citizens are likely to be filtered through the generalized images of citizens that public employees bring to their work in the first place (Lipsky, 2010), a better understanding of these cognitive structures will help further illuminate the cognitive dimension of social categorization and its attitudinal and behavioral outcomes. Implicit theories Scholarship in social cognition suggests that all human cognition and behavior are guided by mental maps, without which it would be impossible to process the massive load of information from the social environment and transform it into actions (Banaji & Greenwald, 2013; Fiske & Taylor, 2021). From this perspective, human cognition is always schematic thinking, as it processes incoming signals through cognitive filters that are stored in long-term memory where those filters have sedimented from past experience and socialization. From this perspective, it follows that the human mind is full of a priori categories and that it therefore largely operates in a mode of categorizing incoming signals from the social environment. As Lipsky (2010) states, when public employees’ work involves people as clients, those employees will have an implicit view of the people they process, just like they have a concept of their work. We refer to the system of interrelated and overlapping categories into which single categories combine as implicit theories (Liegat et al., 2022; Vogel & Werkmeister, 2021). Implicit theories are not formal academic theories, but informal everyday theories that laypeople use to make sense of the social world, including other people, situations, objects, events, and the relationships among these people and things. Implicit theories emerge from prior experiences with the members of the social categories they define, but they may also be learned without primary experience, through socialization (E. R. Smith & Z arate, 1990). The long-term formation of implicit theories, their repetitive use, and their storage at preconscious levels of memory make them difficult to “unlearn”(Fiske & Taylor, 2021). Research has shown that implicit attitudes are not easy to correct intentionally, even if counter-evidence is available (Marvel, 2016). Interventions into implicit attitudes thus often have only short-term effects, with these attitudes bouncing back to their default after a short delay (Lai et al., 2016). This can be a particular problem where implicit theories give rise to stereotyping, prejudice, and discrimination. The “implicit”in implicit theories may suggest that people’s theorizing about the social world is beyond any conscious access and control, but scholarship has shown that, upon closer reflection, people have some introspection into their everyday theories (Lord et al., 2020). However, when in use, implicit theories still predominantly operate at preconscious levels, where important parts of human cognition and decision-making reside, and trigger quasi-automatic responses to stimuli (Kahneman, 2011). Hence, scholarship on implicit theories often evokes dual- 404 IMPLICIT CITIZENSHIP THEORIES process models, which suggest a duality of human information processing (Evans, 2008). In a reflective mode, thinking is slow, rule-governed, and conscious, whereas in a more reflexive mode, it is fast, associative, and unconscious. The latter mode of human cognition comes at the risk of oversimplification and misclassification, but it saves effort, enables fast orientation, and builds strong capacities to act. Implicit theories thus help to optimize the use of limited cognitive resources. The stability of implicit theories contributes further to the principle of cognitive economy (Simon, 1955) because unlearning old and learning new theories itself require cognitive resources. The human brain is therefore inclined to hold on to implicit theories once their benefits in mental processing outweigh the efforts of searching and adopting alternative frames of reference. When applied to people acting in social roles, implicit theories comprise the actors’typical or ideal characteristics (Junker & van Dick, 2014; Lord et al., 2020; Vogel & Werkmeister, 2021). Typical attributes are those that an average representative of this social category is most commonly supposed to show, whereas ideal characteristics are expected from cases at the extremes of the distribution (Junker & van Dick, 2014). Attributes cluster in fuzzy sets of similar traits that reflect and define a specific facet of the social category. Those clusters of semantically related attributes represent prototypes (Operario & Fiske, 2001). Just like the attributes that constitute them, the prototypes themselves are not distinctive, but interdependent. They can be charged with valence, ranging from positive to negative. Implicit citizenship theories Arguably, the citizen’s role is an important social role in society. In political philosophy, scholars have devoted much thinking to the notion of citizenship (Marshall, 1950), with important extensions to the PA literature. Indeed, “[t]he concept of citizenship is closely tied to the modern field of public administration” (Frederickson, 1991, p. 405). This concept often carries a normatively charged image of the citizen in vivid interactions with a democratic administration. For example, Frederickson (1991), in his pleas for a New Public Administration, has coined the notion of the “virtuous citizen,”who is an active, committed, informed, and morally responsible participant in the public policy process. Hence, there is no shortage of academic theories on citizenship, although not all of them outline the good citizen’s desired traits as explicitly as Frederickson’s theory of the public does. From our elaborations above, it follows that, in contrast to academic theories about citizenship, implicit theories are not the outcome of reflective reasoning on rational grounds, but emerge, to a greater or lesser extent, in the minds of laypeople as a result of everyday experiences and socialization. As is the case with any social category with high salience, people will hold implicit assumptions about themselves and others acting in the role of citizens. ICTs, then, are implicit assumptions about citizens’typical or ideal characteristics that guide cognitive categorization and influence attitudes and behaviors toward citizens (Liegat et al., 2022). An exploration into the content and structure of ICTs therefore requires an empirical focus on what people actually think of citizens, instead of a normative reasoning in favor of citizens’desired traits in a theoretical conception of citizenship. Of course, and despite their differences, the contents of implicit theories can still be compared to those of academic theories about citizenship. The extent to which normative conceptions are reflected in what people think of citizens may even be of considerable interest. This is because the extent to which the ideal of a virtuous citizen, for instance, becomes reality is likely to depend on how prevalently participants in state–citizen interactions think along these lines. It also follows from this reasoning that the notion of “citizen”itself is subject to people’s social constructions. From this perspective, the definition of who is a citizen, and who is not, is in the eye of the beholder. The categorization of a target person as a citizen might resonate well with formal criteria of citizenship as defined in academic theories or legal regulations but people might also apply their own criteria, if at all the categorization is a process guided by clear criteria upon which people can reflect. For example, frontline employees might think of all their clients when asked for citizens, notwithstanding that some of them do not conform to formal criteria of citizenship because they are tourists or migrants. ICTs, including the overarching category of “citizen”itself, emerge bottom-up from people’s everyday viewpoints and experiences, rather than top-down from academic theories or legal systems. All people are likely to hold some ICTs, at least in a rudimentary form. This is because it is almost impossible for residents of developed countries not to be exposed to their citizenship, together with the associated rights and duties. However, public employees’ICTs are particularly relevant and interesting. Interactions with citizens are essential to many public service jobs (Guy, 2021; Jakobsen et al., 2019), making this role particularly salient to those working in such jobs. Following research on implicit theories in other fields of social life (e.g., Lord et al., 2020), we assume that the emergence and development of employee–citizen interactions and the outcomes they yield on both sides will, to some extent, depend on the ICTs that public employees bring to their jobs. The potential significance of public employees’ICTs in state–citizen interactions results from comparisons between an observed target’s characteristics and behaviors and the assumptions and expectations that are organized in prototypes (Fiske & Neuberg, 1990; Operario & Fiske, 2001). The degree to which the target (i.e., the PUBLIC ADMINISTRATION REVIEW 405 citizen) matches with a prototype will impact not only the further attributions that the beholder (i.e., the public employee) assigns to the target person, but also the attitudes the beholder develops and the behaviors he/she shows toward the target. The better the match between the target and the prototype, the less cognitive efforts the beholder will need to define the situation and the relationship, and the easier will routinized responses be activated. ICTs therefore help reduce cognitive effort and enable rapid decision making where the role of the citizen is involved and salient in human interactions. DATA, METHODS, AND RESULTS To identify the content and structure of ICTs, we conducted four sequential studies (Figure 1). Studies 1a and 1b shared the goal of generating an initial pool of attributes that public employees associate with citizens. The goal of Study 2 was to examine how these attributes cluster into distinct prototypes of citizens. Finally, Study 3 was confirmatory and evaluated the previously identified structure of prototypes. All four samples were broadly surveyed across a variety of subfields of the public sector in Germany. Since participants could not take part in more than one study, all samples are independent of each other. We provide socio-demographic information in Table 1and more detailed information on the sampling procedure in Supporting Information A. Through all four studies, public sector employees were asked about their associations with citizens in the context of their daily work in their jobs. Given that ICTs are likely to be contingent on broader traditions of statehood and citizenship, the focus on any empirical setting makes the transferability of the results to other national settings an interesting question for further research. The German political context is characterized by a strong separation of powers, a multi-level federalism, and high levels of professionalism in the public service (O’Toole & Meier, 2017). In the continental European rule-of-law tradition, of which Germany is a prime example, a stronger separation between state and society imbues the relationship between the state and its citizens with more hierarchy than in the Anglo-Saxon tradition (Kuhlmann & Wollmann, 2019; Peters, 2021). However, like many other countries, Germany has made considerable efforts to improve the quality of public services at the frontline (Pollitt & Bouckaert, 2017), with increasing integration of citizens into collaborations with public employees (Grohs, 2021). Such traditions and changes are likely to affect how public employees think of citizens, making ICTs interesting for comparisons across temporal and geographical settings. Studies 1a and 1b: Attribute generation Study 1a We conducted 28 face-to-face interviews with frontline employees in the German public sector (Table 1, Supporting Information B). Interviewees were recruited from all subfields of the public sector and regularly interacted with citizens during their daily work. The interviews were semi-structured and followed the critical incident technique (Flanagan, 1954). We focused on how frontline employees describe citizens in job-related situations, starting from more common situations and moving to particularly positive and negative experiences (for the interview guideline, see Supporting Information C). We asked how interviewees experienced citizens in such situations and what attitudes and behaviors they showed. Given that the interviews were conducted during the COVID-19 pandemic, we asked participants to think of their jobs up until the pandemic to prevent results from being contaminated with influences from extraordinary circumstances. It should be noted, however, that this strategy comes at the expense of potential episodic recall biases, which might occur when participants report about events in the past (Resh et al., 2020). In the analysis of the interview transcripts, we generated codes for every citizens’attribute that was either directly mentioned by the interviewees or apparent from their narrations. This procedure resulted in a total of 829 attributes. Since many attributes were synonyms (e.g., “smart”and “intelligent”), we further consolidated the coding system and ultimately arrived at 389 distinct codes (i.e., attributes). Study 1 b: Attribute generation Quantitative survey 83 core attributes Study 1 a: Attribute generation Semi-structured interviews a) 6 prototypes with 34 attributes b) 6 prototypes with 17 attributes 829 attributes 923 attributes 6 prototypes with 17 attributes Quantitative survey EFA Study 2: Prototype identification Quantitative survey CFA Study 3: Prototype validation Network analysis 558 attributes Consolidation of attributes FIGURE 1 Overview of studies. 406 IMPLICIT CITIZENSHIP THEORIES Study 1b To balance the depth of our qualitative study with the breadth of a more large-scale study, and in line with comparable studies (e.g., House et al., 1999), we launched a complementary survey among 204 public employees in Germany (Table 1, Supporting Information A). After responding to some demographic questions, participants were requested to state at least five attributes that immediately come to their mind when thinking of citizens (i.e., "Please think carefully about your current work. From this perspective,what are the immediate associations that come to your mind when you think of citizens?"). A total of 923 attributes were provided as open-text responses and coded into 291 unique attributes. We subsequently combined the results of Studies 1a and 1b and compiled a list of core attributes by which public employees characterize citizens. The number of non-redundant attributes in the combined list, as aggregated from both studies, was 558. From this input data, we compiled associative networks (separately and combined for the attributes resulting from both studies) by TABLE 1 Descriptive statistics of samples. Study 1a (n=28) Study 1b (n=204) Study 2 (n=611) Study 3 (n=860) n(%) Mean (SD) n(%) Mean (SD) n(%) Mean (SD) n(%) Mean (SD) Age n/a 45.6 (12.3) 45.7 (12.6) 44.2 (12.4) Gender Male 15 (46.4) 91 (44.6) 296 (48.5) 382 (44.4) Female 13 (53.6) 113 (55.4) 313 (51.2) 477 (55.5) Others 0 (0.0) 0 (.0) 2 (0.3) 1 (0.1) Tenure (in years) n/a 19 (13.2) 18.7 (13.3) 17.5 (12.9) Pay group E1-E4 or A2-A4 n/a 13 (6.4) 37 (6.0) 79 (9.2) E5-E8 or A5-A9 n/a 79 (38.7) 248 (40.6) 322 (37.4) E9-E12 or A9-A13 n/a 74 (36.3) 232 (38.0) 306 (35.6) E13-E15 or A13-A16 n/a 32 (15.7) 59 (9.7) 105 (12.2) Non-tariff or B-grade salary n/a 6 (2.9) 35 (5.7) 48 (5.6) Administrative level Federal government n/a 34 (16.7) 94 (15.4) 161 (18.7) State government n/a 77 (37.7) 235 (38.5) 321 (37.3) Local government n/a 73 (35.8) 209 (34.2) 271 (31.5) Social security administration n/a 20 (9.8) 47 (7.7) 66 (7.7) Others n/a 0 (.0) 26 (4.2) 41 (4.8) Administrative subfields General public services 5 (17.9) 45 (22.1) 91 (14.9) 106 (12.3) Education 4 (14.3) 45 (22.1) 123 (20.1) 206 (24.0) Recreation, culture, and religion 2 (7.1) 4 (2.0) 14 (2.3) 29 (3.4) Public health services 2 (7.1) 40 (19.6) 101 (16.5) 150 (17.4) Public order and security 6 (21.4) 26 (12.7) 108 (17.7) 110 (12.8) Environmental protection and nature conservation or waste management 1 (3.6) 5 (2.4) 16 (2.6) 29 (3.4) Defense 1 (3.6) 3 (1.5) 21 (3.4) 30 (3.5) Economic affairs 2 (7.1) 15 (7.3) 40 (6.6) 69 (8.0) Housing 1 (3.6) 7 (3.4) 27 (4.4) 24 (2.8) Social security 4 (14.3) 11 (5.4) 58 (9.5) 100 (11.6) Others 0 (0.0) 3 (1.5) 12 (2.0) 7 (0.8) Type of work a n/a 5.3 (3.1) 5.1 (3.1) 4.9 (3.0) Citizen contact b n/a 49.2 (33.9) 42.9 (35.6) 40.5 (34.8) a 0=exclusively imposing obligations, 10 =exclusively granting benefits. b In percentage of working time. PUBLIC ADMINISTRATION REVIEW 407 analyzing co-occurrences of attributes in responses from the same interviewees or survey participants, respectively (for a similar procedure, see Asseburg et al., 2020). To reduce the number of items, we applied a core/periphery partition to the networks (Borgatti & Everett, 2000) and removed all peripheral attributes, which resulted in a list of 85 non-redundant attributes in the network cores. We removed two items from this list, as they were difficult to interpret unambiguously. The final pool of attributes that resulted from Studies 1a and 1b therefore comprised 83 items (Appendix A). Study 2: Prototype identification The purpose of Study 2 was to explore how the attributes cluster in prototypes of citizens and, hence, to detect structures in ICTs. This was achieved in an online survey using an RRM (Asseburg et al., 2020; Meade et al., 2020). Like other speeded categorization tasks (e.g., Damanskyy et al., 2023; Galdi et al., 2008), an RRM exposes participants to stimuli and asks participants to provide fast responses. The idea behind these methods is that participants reveal more intuitive and associative attitudes toward the object of evaluation when they respond quickly and spontaneously (Galdi et al., 2008). Time pressure limits the possibilities of reflecting more explicitly on the task or question and providing more controlled answers (Komar et al., 2010), which should also reduce the risk of social desirability. Methods that increase time pressure for participants have successfully been transferred to PA scholarship, including the Implicit Association Test (Marvel, 2016)and the RRM itself (Asseburg et al., 2020). The RRM also offers the practical advantage that, due to the speeded condition, it allows for an evaluation of a large number of items in a reasonable amount of time. Participants were exposed to the 83 attributes resulting from Studies 1a and 1b and categorized each of these attributes under speeded conditions. The attributes appeared on the screen sequentially and in random order, together with the term “citizens” (Supporting Information D). The task was to categorize the attributes into one of two options: “match”or “no match”with citizens. The participants were instructed as follows: “Please think carefully about your working life and the professional contact you have with citizens. […] In the following, we will show you different terms. Please decide spontaneously whether the term fits citizens or not”(for the complete instructions, see Supporting Information E). Before analyzing the data, we excluded observations from subjects who constantly pressed only one button (n=17) or whose answers to screening questions made no sense (n=33). In addition, we only considered responses within a time of 300 to 10,000 milliseconds (ms). These cut-off criteria ensured that the observations conformed to what is widely accepted in the literature as quasi-automatic and largely implicit responses to perceptual stimuli (Greenwald et al., 2003). Furthermore, we identified response times exceeding the mean by more than two standard deviations (SDs) as outliers and removed them (Asseburg et al., 2020; Greenwald et al., 2003). We followed common procedures and imputed these missing values by the mean of valid answers for each attribute (Lin & Tsai, 2020). We also conducted the analyses without this adjustment and did not find meaningful differences. In sum, we imputed 2708 outliers, which resulted in a final sample containing 50,713 responses from 611 participants (Table 1for descriptive statistics). The average speed of valid responses was 1380.1 ms with an SD of 448.3 ms. Data were analyzed using R version 4.1 (R Core Team, 2023) and the lavaan package 0.6–10 (Rosseel, 2012). Except for the imputed values, the ratings of the attributes were binary (i.e., “match”or “no match”). Due to the data structure, we calculated a tetrachoric correlation matrix which, in turn, fed into an EFA with maximum likelihood estimation (Finney & DiStefano, 2013) and oblimin rotation. Before analyzing the factor structure, we checked whether the data were suitable for the EFA. The sample of 611 participants reflected an appropriate size for the factor analysis (Fabrigar et al., 1999). The Kaiser-Meyer-Olkin measure of sampling adequacy indicated strong relationships among variables (KMO =.900). We performed several rounds of the analysis, and in each round, we removed attributes with a loading below .40 from further analysis. The number of factors to be extracted was guided by parallel analysis (Horn, 1965) and the Kaiser-Guttman criterion (i.e., eigenvalues above 1). These criteria indicated that five or six factors emerged from the data. We decided in favor of the sixfactors solution, which contains 34 attributes, because it explained more variance than the five-factors solution (54.4 vs. 50.9 percent). After a content validation, we labeled the resulting factors Engagement,Kindness,Determination,Reticence,Neediness, and Foreignness (Table 2). As an alternative to this solution with 34 attributes, we followed Sy’s( 2010) strategy to limit the number of attributes further, which increases practicality in future studies (Epitropaki & Martin, 2004; Vogel & Werkmeister, 2021). For each factor, we only considered the three attributes with the highest loadings. Foreignness consisted of only two attributes, which were kept. This solution contains 17 attributes (Table 2, in italics). Study 3: Prototype validation The goal of Study 3 was to cross-validate the factor structure derived from Study 2 in an independent sample and to decide which of the two alternative solutions (i.e., 34 vs. 17 items) to retain. Accordingly, we followed the same procedure as in Study 2 but limited the item set to the 408 IMPLICIT CITIZENSHIP THEORIES 34 attributes of the six-factors solution from Study 2. Again, observations were deleted if participants consistently pressed only one button (n=27) or responded nonsense to screening questions (n=5). 1432 outliers were imputed by the mean of valid answers per attribute, such that the final sample contained 29,240 responses from 860 participants (response time: M=1489.8 ms, SD =445.3 ms; Table 1). We used robust diagonally weighted least squares (DWLS) CFA to examine the validity of the six factors extracted in Study 2. This method is suggested by the attributes’binary and non-normal data structure, in line with recommendations in the literature (e.g., Finney & DiStefano, 2013). We first tested the six-factor model containing 34 attributes. Results indicated that this model provided a suboptimal fit to the data (X 2 /df =4.526; robust CFI =0.866; robust TLI =0.853; robust RMSEA =0.082; SRMR =0.103) (Hu & Bentler, 1999). By contrast, alternative model specifications containing 17 attributes showed a substantially better fit (Supporting Information F). Results indicate that a first-order sixfactors model provides the best fit with the data (X 2 / df =3.076; robust CFI =0.960; robust TLI =0.948; robust RMSEA =0.049; SRMR =0.067). Figure 2illustrates the TABLE 2 Factor matrix (Study 2). Factor label Attribute Factor loading Communality M SD Engagement (15.6%) a Motivated .76 .70 .51 .49 Goal-driven .75 .54 .52 .48 Conscientious .74 .64 .47 .49 Dedicated .70 .58 .52 .49 Informed .67 .50 .54 .49 Inquisitive .65 .48 .54 .49 Supportive .65 .51 .46 .48 Hardworking .60 .43 .52 .49 Interested .53 .64 .72 .44 Fierce .46 .21 .40 .48 Kindness (10.1%) a Friendly .87 .73 .79 .39 Nice .77 .64 .78 .41 Polite .67 .65 .70 .45 Disturbing .55 .56 .32 .45 Unpleasant .47 .63 .40 .48 Determination (8.7%) a Egoistic .85 .78 .49 .49 Assertive .59 .49 .46 .49 Demanding .55 .43 .75 .42 Unreasonable .53 .75 .56 .48 Bossy .52 .53 .51 .48 Discerning .47 .20 .70 .45 Reticence (8.0%) a Introvert .77 .70 .28 .43 Reserved .76 .60 .36 .46 Shy .69 .63 .30 .45 Closed .58 .52 .31 .45 Passive .53 .35 .38 .47 Neediness (7.4%) a Anxious .78 .69 .48 .49 Desperate .64 .58 .52 .49 Worried .60 .40 .70 .44 Burdened .53 .41 .57 .48 In need (of help) .51 .30 .71 .44 Insecure .50 .37 .67 .46 Foreignness (4.5%) a Foreign-language .86 .76 .56 .49 Non-native .73 .56 .57 .48 Note:n=611; items from the 17-attributes solution in italics. a Proportion of variance explained. 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Hatmaker. 2021. “Towards Understanding Workplace Incivility: Gender, Ethical Leadership and Personal Control.”Public Management Review 23(1): 31–52. https://doi.org/ 10.1080/14719037.2019.1665701. 416 IMPLICIT CITIZENSHIP THEORIES AUTHOR BIOGRAPHIES Rick Vogel is Full Professor of Public Management at Universität Hamburg (Germany). His current research interests are public sector leadership, cross-sector partnerships, state–citizen interactions, and institutional change in the public sector. Email: rick.vogel@ uni-hamburg.de Dominik Vogel is an Assistant Professor of Public Management at Universität Hamburg (Germany). His research is focused on leadership in the public sector, motivation of public employees, interaction of citizens and administration, and behavioral public administration. Email: dominik.vogel-[email protected] Marlen Christin Liegat is a Research Assistant and PhD candidate at the Junior Professorship of Public Management at Universität Hamburg (Germany). Her research interests are how psychological mechanisms affect public sector workers in state–citizen interactions. Email: [email protected] David Hensel is a Postdoctoral Researcher at the Chair of Public Management at the Universität Hamburg (Germany) and at the Chair of Marketing at the Helmut-Schmidt-University (Germany). His current research interests are implicit measurement methods and implicit citizenship theories. Email: david.hensel@ uni-hamburg.de SUPPORTING INFORMATION Additional supporting information can be found online in the Supporting Information section at the end of this article. How to cite this article: Vogel, Rick, Dominik Vogel, Marlen Christin Liegat, and David Hensel. 2025. “From Social Categorization to Implicit Citizenship Theories: Advancing the Socio-Cognitive Foundations of State–Citizen Interactions.”Public Administration Review 85(2): 402–418. https://doi.org/10.1111/puar.13844 PUBLIC ADMINISTRATION REVIEW 417 APPENDIX A: ATTRIBUTE LIST APPENDIX B: MODEL COEFFICIENTS OF CFA FOR THE ICT MODEL Affable Empathetic Open Agitated Expectant Passive a Aggressive Extrovert Pleasant Annoying b Fierce Polite Anxious Foreign-language Preloaded Appreciative Friendly Reproachful Ashamed Furious c Reserved Assertive Goal-driven Satisfied Assisting Grateful Seeking help Attention-seeking Greedy b Sensitive Bossy Hardworking Shy Burdened a,c Impatient Supportive Calm In need (of help) Sympathetic Closed Informed Tiring Conscientious Inquisitive Trusting Conspicuous Insecure Uncomprehending c Control-addicted Insistent Uneducated Cooperative Interested Unfriendly Dedicated Introvert Uninformed Demanding Irritated Uninterested Desperate a Lazy Unknowing Difficult Likeable Unnerving Disadvantaged Loud Unpleasant Discerning Lying Unreasonable Dismissive Motivated Unsatisfied Disorganized Nagging Vulnerable Disturbing Nice Worried a,c Egoistic a,c Non-native Emotional Obstinate Note: Attributes from Studies 2 and 3 (attributes from Study 3 in italics); a Also training attribute in Study 3. b Deleted because of ambiguous interpretability. c Also training attribute in Study 2. Prototype Attribute βSE z Engagement Motivated .882*** .031 28.912 Conscientious .786*** .032 24.320 Goal-driven .619*** .039 15.859 Kindness Friendly .916*** .021 43.563 Polite .894*** .021 42.572 Nice .874*** .023 38.501 Determination Egoistic .883*** .039 22.868 Demanding .696*** .045 15.344 Assertive .668*** .039 16.988 Reticence Shy .765*** .048 15.905 Reserved .710*** .045 15.762 Introvert .530*** .050 10.674 Neediness Desperate .875*** .031 28.512 Anxious .783*** .032 24.119 Worried .678*** .039 17.429 Foreignness Non-native .901*** .052 17.464 Foreign-language .817*** .049 16.739 Note:n=860. ***p< .001. 418 IMPLICIT CITIZENSHIP THEORIES