Following New Public Management reforms, European higher education systems increasingly operate as an “evaluative state” (Neave, 1998) in which universities are steered through performance measurements and indicator‑based funding (de Boer et al., 2007). In this context, universities tie internal decision‑making more closely to external indicators as part of strategic performance management (Krücken et al., 2015). Furthermore, universities are expected to act strategically in competitive markets while maintaining their public mission and institutional autonomy (Hüther & Krücken, 2018; Leišytė, 2025). In Germany, performance-oriented governance reforms, including funding models and target agreements, have strengthened university leadership and tied internal decision-making more closely to external expectations (Schmid & Wilkesmann, 2015; Wissenschaftsrat, 2018). Hence, digital student recruitment can be framed as a strategic instrument for strengthening competitive positioning and presenting institutional priorities (Hochschulforum Digitalisierung, 2016; Witte, 2025). This is a key arena where conflicts between autonomy, market pressures, and academic values become visible (Pucciarelli & Kaplan, 2016; Marginson, 2024), particularly in computer science, a field characterized by strong labor-market demands and intense competition for students (Wissenschaftsrat, 2020). Thus, this paper asks the following research question: How does performance-oriented governance in German higher education shape the organizational management of digital student recruitment in computer science study programs?

A multilevel theoretical approach is used to link institutional positioning and study choice, examining the organizational management of digital recruitment in the field of computer science. Drawing on Marginson's (2006) concept of higher education as a stratified field, this study frames digital student recruitment as a concrete form of positioning used by institutions to compete for status and visibility. Perna's (2006) integrated model explains study choice as shaped by demographic characteristics and the higher education context, including access to information and perceived fit.

The study follows a sequential explanatory mixed-methods design (Creswell & Plano Clark, 2018). First, a representative student survey in computer science programs (N = 2,601) is analyzed in SPSS using regression and clustering procedures to examine perceived helpfulness and availability of digital information sources. Secondly, eight institutional case studies from public and private German higher education institutions were conducted, combining interviews (N = 19) with website analysis. These case studies were analyzed in MAXQDA using thematic qualitative content analysis (Kuckartz, 2014). Findings are integrated through a convergence coding matrix (Miles et al., 2014).

Quantitative findings indicate that students mainly rely on institutional and program websites, suggesting that more interactive formats are available only to a limited extent. Students have frequently reported an absence of video-based and social media-related information. Hense, multimedia and peer-oriented formats appear to be less established and are often perceived as unavailable rather than unhelpful. This indicates that the official website serves as a reference point across institutions, while the broader digital channel portfolio varies in quality and scope.

Qualitative findings suggest that in the German context, digital student recruitment follows three organizational models aligned with institutional type. Private institutions generally follow a market-driven model with centralized coordination, dedicated roles, and integrated multichannel recruitment. This model supports continuous maintenance and consistent visibility across platforms. Public universities of applied sciences frequently adopt a grassroots model, wherein outreach initiatives are driven by selected faculty members, program coordinators, and student-driven initiatives. This approach can result in the generation of credible content. However, the sustainability and systematic visibility of these initiatives are dependent on individual engagement, which can vary. Public universities employ a traditional model centered on administratively maintained websites that prioritize comprehensive information provision. Social media and multimedia formats remain selective, decentralized, or only loosely connected to recruitment routines. The key difference across cases is how recruitment work is embedded within the organization.

In conclusion, the findings reveal that performance-oriented governance does not produce a homogeneous recruitment logic. Instead, its impact is mediated by internal structures, which ultimately determine whether an institution moves beyond basic information provision toward a strategic, multichannel digital presence. This study's integration of a large-scale representative survey with in-depth case studies is a particular strength, providing a comprehensive view of both student needs and organizational logic. While the emphasis on computer science in Germany restricts the generalizability to other disciplines or national contexts, the identified organizational models offer a framework replicable for future studies.

Artificial intelligence (AI) is increasingly recognised as a transformative force in higher education. While most research focuses on teaching and learning, administrative domains remain underexplored (Crompton & Burke, 2023). At the same time, universities operate in competitive environments where communication and reputation are closely tied to public trust (Marginson, 2006). Marketing and communication departments have therefore gained strategic importance as boundary-spanning units that shape institutional narratives (Hemsley-Brown & Oplatka, 2006; Nicolescu, 2009). As AI tools become integrated into these processes, they are likely to influence not only operational practices but also governance and authority relations (UNESCO, 2023).

Universities are commonly described as professional bureaucracies and loosely coupled systems, with authority located in the academic core while administrative units serve supportive functions (Mintzberg, 1979; Weick, 1976). However, the professionalisation of administrative roles and the growing strategic relevance of functions like marketing have begun to challenge these arrangements (Gornitzka & Larsen, 2004; Whitchurch, 2013). AI may accelerate these developments by creating new data dependencies, empowering specialised staff, and giving rise to hybrid roles between administrative and academic domains (Mahroof et al., 2025; Selten & Klievink, 2024). Whether this centralises or distributes authority remains an open question (Cortellazzo et al., 2026).

This study examines how AI adoption in university marketing departments reshapes governance structures and authority relations. Rather than treating AI as a purely technical matter, it approaches adoption as an organisational process affecting decision-making authority, professional autonomy, and control distribution between actors (Musselin, 2007). Marketing units provide an instructive case because they sit at the boundary between the university and its environment, where questions of legitimacy and institutional communication are central (Rughini et al., 2025). The study responds to calls for research that moves beyond descriptive accounts of AI implementation towards analytical frameworks capturing the institutional dynamics of technological change (Azevedo et al., 2025).

The study is guided by the following research question:
How does AI adoption in university marketing departments reshape governance structures and authority relations within different higher education institutions in Germany?

Empirically, the study adopts a qualitative multiple-case design focusing on marketing departments at German higher education institutions. The sample includes three to four institutions within each of four organizational types: public universities, public universities of applied sciences, private universities, and private universities of applied sciences resulting in a total of 12–16 cases. This selection reflects the institutional diversity of the German higher education system (Hüther & Krücken, 2018).

Data collection combines document analysis of institutional AI strategies and governance frameworks with semi-structured interviews. The sample includes marketing professionals, university leadership involved in strategic communication, IT staff responsible for AI infrastructure, and academic actors such as deans or faculty representatives. This multi-perspectival approach captures how authority is negotiated across organisational boundaries (Yin, 2018). The analysis follows a thematic approach, focusing on how AI-related practices are interpreted and negotiated within organisational contexts (Braun & Clarke, 2006), with particular attention to shifts in decision-making authority between administrative and academic actors.

By examining AI adoption in a boundary-spanning unit, the study contributes to debates on governance and management in higher education. It asks how technological innovation redraws authority lines within professional bureaucracies and what this means for a university’s ability to communicate credibly with the outside world. In doing so, it moves beyond descriptions of what AI tools can do towards understanding what their adoption actually changes inside organisations (Azevedo et al., 2025; Selten & Klievink, 2024).

Background and Research Question

AI-based systems have entered everyday life in higher education institutions and are now applied in teaching, learning, and administration. In an increasingly competitive higher education system, where institutions, their organizational units and individuals compete for reputation, resources, and visibility (Krücken et al., 2021; Hart & Rodgers, 2024), communicative AI (ComAI), such as Chatbots and AI-based learning assistants, represents a particularly disruptive development. Unlike earlier technology, ComAI reaches into our communicative practices, participates directly in dialogic processes, fundamentally altering how knowledge is transmitted and interactions are structured.

However, the appropriation of ComAI by higher education staff varies considerably. We understand ComAI appropriation as a complex, context-dependent process embedded in a specific socio-cultural context (Jaeggi, 2022), which can serve as both a means of empowerment and a mechanism of differentiation manifesting either as individual or collective practice (Deinet & Reutlinger, 2004). In addition, the use of ComAI could be driven by competitive pressures operating at multiple levels.

Within higher education institutions, academic disciplines are clustered in schools and departments. Over decades, different academic disciplines have developed their own epistemological traditions and value systems. These "tribes and territories" (Becher & Trowler, 2001) shape not only how knowledge is produced but also how new technologies are appropriated and embedded in competitive dynamics. Within their respective departments, scholars compete for limited space in high-ranking journals, positions at prestigious universities, and funding (Kosmützky & Meier, 2025). At the departmental level, competition occurs not only externally with comparable faculties at other institutions but also internally for resources, visibility, and strategic significance within their own institution (Bloch et al., 2024; Krücken et al., 2021). Appropriation thus becomes a site where disciplinary cultures and competitive logics intersect. Against this background, this paper examines how different disciplinary cultures in HEIs perceive and appropriate ComAI and what competitive advantages they see in its use.

Theoretical Framework

We combine three theoretical perspectives to capture the complex relationships between disciplinary cultures, technology appropriation, and competitive dynamics.

(1) Becher and Trowler's (2001) concept of academic cultures as "tribes and territories" with distinct epistemological traditions, research methods, communication styles, and value systems. These may also influence appropriation of new technologies and competitive dynamics.

(2) Norbert Elias's figurational sociology enables us to conceptualize schools and departments as "dynamic networks of mutual dependencies" (Elias, 2001), constituted through the interaction of various actors (students, faculty, administrative staff). In this context, ComAI is understood as an element of hybrid figurations that is integrated into existing social structures and enables new options for action (Hepp et al., 2023).

(3) Sociology of competition by Georg Simmel (1908) emphasizes that competition is not only divisive but also connecting: competing actors observe each other, learn from each other, and align their actions with one another. In the higher education context, this means that departments do not develop in isolation but align their strategies in relation to other faculties.

Methodological Approach

Our empirical investigation combines quantitative survey data from the Center for Higher Education on AI use by students with semi-structured interviews with deans/department heads at five German higher education institutions. Selection criteria included: (1) funding through "AI in Higher Education" 2020-2025, (2) publication of a current AI policy, and (3) regional diversity. The focus lies on comparing social sciences, economics, and engineering. In the interviews, deans are questioned as representatives of their respective figurations. The analysis is conducted using qualitative content analysis following deductive categorization.

Expected Results and Relevance

The study aims to draw a differentiated picture of discipline-specific appropriation of ComAI, making visible both the different perceptions and strategic uses across various disciplinary cultures. At the empirical level, significant differences between the examined departments are expected. Furthermore, the study aims to show to what extent departments perceive and use ComAI as a strategic instrument for gaining competitive advantages. At the theoretical level, the study contributes to better understanding the interactions between disciplinary cultures and competitive dynamics in the context of technological change. For higher education management, the study provides insights for strategic development: it demonstrates that a "one-size-fits-all" approach to implementing ComAI does not do justice to disciplinary specificities and that successful strategies must consider the different needs of disciplinary cultures.