Training Doctoral Researchers for Applied Computing Research: Design Science and Action Research in International Contexts

Definition

Doctoral training in applied computing and information systems is the structured development of a researcher’s capacity to produce original, rigorous, and scholarship that is relevant to practice, supported through doctoral supervision, which provides academic guidance for research design decisions, progress management, scholarly quality, and researcher development. In this setting, Design Science Research (DSR) is a methodology that generates knowledge through the purposeful design and evaluation of an artifact intended to address a defined problem. In parallel, Action Research (AR) generates knowledge through collaborative, iterative cycles of planned action and critical reflection conducted with stakeholders in real settings. Bringing both traditions together, Action Design Research (ADR) integrates DSR and AR by developing and evaluating artifacts through participatory cycles focused on intervention while maintaining explicit expectations of rigor and contribution. These approaches are often used in international or study abroad research contexts, which are research environments spanning national, cultural, institutional, or governance boundaries and therefore require adaptive methods, careful ethical attention, and sustained stakeholder engagement. This synthesis results in an integrated methodological framework that positions Action Design Research as a supervisory scaffold for doctoral training in applied computing and information systems. The framework integrates Design Science Research and Action Research within an iterative cycle embedded in dialogical supervision and ethical reflexivity. It contributes a structured model for aligning methodological rigor, doctoral learning, and practical impact in complex and international research environments.

1. Introduction

Doctoral education in computing and information systems (IS) is undergoing a period of significant transition. Traditionally, doctoral training in these fields has emphasized theoretical contribution, technical specialization, and methodological rigor, often prioritizing abstract problem formulations and controlled research settings. While this model has produced substantial advances in foundational knowledge, it has increasingly been challenged by the growing demand for research that demonstrates practical relevance, societal impact, and engagement with complex real-world problems. Across academia, industry, and the public sector, doctoral graduates are now expected not only to contribute to theory but also to design, evaluate, and implement solutions that address organizational and societal challenges under conditions of uncertainty and contextual variation. Recent bibliometric analyses indicate a significant global expansion in scholarship on international doctoral students, reflecting the growing structural transformation of doctoral education across national and institutional contexts [1]. This expansion highlights increasing mobility, diversification of doctoral populations, and heightened expectations for cross-context research competence.

These shifting expectations have placed new pressures on doctoral supervision and training practices, particularly in applied computing domains such as cybersecurity, data science, information systems, and socio-technical system design. Contemporary research highlights how supervisory relationships are shaped by identity formation, relational dynamics, and institutional power structures, underscoring the complexity of doctoral learning environments [2,3]. Supervisors are increasingly tasked with guiding doctoral researchers through research processes that involve stakeholder engagement, iterative development, ethical decision-making, and contextual adaptation activities that extend beyond traditional laboratory-based or purely analytical paradigms. Empirical accounts of international doctoral journeys describe how motivation, institutional structure, and cultural negotiation shape supervisory expectations and research progression, highlighting the need for adaptive and context-sensitive doctoral training models [4]. At the same time, intercultural and cross-boundary supervision arrangements introduce additional layers of epistemological negotiation and goal alignment [5,6]. These developments point toward the need for methodological approaches that explicitly integrate rigor with contextual responsiveness and experiential learning.

Design Science Research (DSR) and Action Research (AR) have emerged as two prominent methodological traditions capable of addressing these evolving demands. Both approaches emphasize intervention, iteration, and reflection, positioning the doctoral researcher as an active contributor to change rather than a detached observer. Within computing and IS research, DSR has been widely adopted for the development and evaluation of artifacts such as models, systems, and methods, while AR has been employed to study and improve practices within organizations and communities through cyclical processes of action and reflection. Beyond their methodological contributions, these approaches offer structured pathways for developing key doctoral competencies, including problem framing, stakeholder collaboration, evaluative reasoning, and reflexive practice. Recent discussions of supervisory practice further highlight how feedback processes and emerging generative artificial intelligence tools are reshaping doctoral mentoring and assessment environments, raising new considerations for methodological scaffolding and supervisory design [7,8].

The relevance of DSR and AR becomes particularly pronounced in international and study-abroad research contexts. Doctoral research conducted across national, cultural, and institutional boundaries introduces additional layers of complexity for both researchers and supervisors. Differences in infrastructure, governance structures, ethical norms, linguistic diversity, and power relations can significantly shape research design and execution, requiring heightened methodological flexibility and ethical awareness [9]. In such environments, traditional supervisory models may prove insufficient, underscoring the need for integrative frameworks that support adaptive, participatory, and context-sensitive research practices while maintaining expectations of rigor and contribution. Studies of equity and distance supervision further reveal how international doctoral education is increasingly shaped by digital mediation, structural asymmetries, and access disparities, intensifying the supervisory and ethical considerations associated with cross-border doctoral research [10].

This entry synthesizes scholarship on doctoral supervision and training in computing and IS, with particular attention to the pedagogical and supervisory implications of Design Science Research and Action Research. By integrating insights from information systems methodology, higher education research, and contemporary supervision studies, it develops a structured conceptual framework that positions Action Design Research as an integrative supervisory scaffold. The entry examines how DSR and AR, individually and in combination, can support the development of doctoral researchers capable of producing rigorous, impactful, and ethically grounded research in complex international contexts, and it outlines implications for doctoral supervision, program design, and future methodological development.

This entry synthesizes scholarship from information systems, computing education, and higher education research to examine how doctoral supervision and training intersect with Design Science Research, Action Research, and Action Design Research in applied computing contexts. The objective of the review is conceptual and integrative rather than meta-analytic.

Relevant literature was identified through targeted searches of scholarly publications and through backward and forward citation tracking to locate foundational and influential works related to doctoral supervision, applied research methodologies, and international doctoral training environments. Particular attention was given to studies addressing methodological rigor, experiential learning in doctoral research, and supervision practices within complex or practice-oriented research settings. The analysis followed a thematic and interpretive synthesis approach. Sources were examined to identify recurring themes related to supervision models, methodological alignment, reflexive practice, and international research contexts. These themes were integrated to develop the conceptual framework presented in this entry. No new empirical data were collected for this study.

2. Doctoral Supervision and Researcher Development in Computing and Information Systems

Doctoral supervision in computing and information systems operates primarily through apprenticeship-based learning structures in which doctoral researchers develop methodological competence, disciplinary positioning, and scholarly identity through sustained supervisory interaction [11,12]. Within this framework, supervision functions as a pedagogical and developmental process through which students acquire research practices, academic norms, and evaluative standards [12,13]. Supervisory engagement also shapes doctoral researchers’ socialization into disciplinary writing conventions and academic voice, particularly in international contexts where linguistic and epistemic expectations may vary across institutions [14,15]. At the program level, supervisory relationships are structured around formal milestones such as proposal approval, methodological training, and publication benchmarks, reflecting institutional mechanisms for quality assurance and progression [16,17]. Longitudinal analyses of doctoral theses further demonstrate shifts in scholarly stance and authorial positioning, underscoring the supervisory role in shaping academic identity formation [18].

Despite its strengths, this traditional approach has been increasingly questioned, particularly in applied computing and IS research. A longstanding concern in the literature is the tension between rigor and relevance, where research that meets high academic standards may appear disconnected from practice, while work that addresses real-world problems risks being viewed as insufficiently theoretical or methodologically robust [19,20]. This tension is especially visible in socio-technical computing fields, where research problems emerge from the interaction of people, organizations, and technology, and where external stakeholders often influence both problem definitions and success criteria [20,21]. As areas such as cybersecurity, digital governance, analytics, and artificial intelligence increasingly demand demonstrable impact, doctoral supervision is under pressure to support research that remains academically rigorous while engaging directly with practical constraints and implementation realities [9,13].

In response to these shifts, the expectations placed on doctoral researchers have broadened. Beyond technical expertise and methodological competence, doctoral training now emphasizes abilities such as framing problems in complex and ambiguous settings, working iteratively with evolving research questions, exercising ethical judgment, and communicating effectively with non-academic stakeholders [22,23]. These skills reflect broader transformations in doctoral education, where knowledge production is increasingly collaborative, context-dependent, and shaped by societal and professional demands [14]. Research on supervision further suggests that supervisors and doctoral researchers may hold different, often unspoken, assumptions about what effective supervision entails, ranging from project management and quality control to intellectual mentorship and identity development, which can create tension if expectations are not made explicit [6,24]. Recent work further emphasizes the relational, identity-based, and power-sensitive dimensions of supervision, highlighting how supervisory relationships shape doctoral identity formation and working conditions across diverse institutional contexts [2,5,25].

As a result, the supervisory role itself has evolved. Rather than focusing solely on guiding independent scholarships, supervisors are increasingly expected to support learning within complex research environments that involve uncertainty, iteration, and external engagement [6,8]. At the same time, doctoral supervision has become more transparent and externally accountable through reporting requirements, progress monitoring, and formalized assessment structures, creating a more regulated supervisory context [10,26]. These conditions can intensify the relational and emotional dimensions of supervision, particularly when doctoral research involves fieldwork, organizational negotiation, or shifting research trajectories [15,27].

These challenges are especially pronounced in computing and IS, where rapid technological change and evolving regulatory landscapes can destabilize linear research planning. Doctoral projects often engage emerging technologies and dynamic socio-technical systems, making it difficult to define fixed research designs or stable contribution claims at the outset [12,13]. Consequently, doctoral supervision must support adaptive learning processes that allow research objectives, methods, and evaluation criteria to evolve over time rather than remain static.

Taken together, these developments help explain the growing interest in methodological approaches that explicitly support learning-by-doing, structured iteration, and engagement with practice in doctoral training, particularly in applied computing research. Design Science Research and Action Research have gained prominence in this regard because they legitimize iterative cycles of intervention, evaluation, and reflection while maintaining expectations of scholarly contribution and methodological rigor [12,13]. The following sections examine these approaches in greater detail, considering how they can be understood not only as research methodologies but also as supervision-friendly training frameworks, and how their integration may be especially valuable in international research contexts.

3. Design Science Research as a Supervision-Friendly Training Framework

DSR is now a mainstream approach in information systems and applied computing for producing knowledge that is both scholarly and usable. Rather than treating practice as a backdrop, DSR begins with a real problem and responds by designing and evaluating an artifact, such as a model, method, framework, or software system, while still requiring clear contributions to knowledge and evidence of rigor [12,27]. This orientation helps explain why DSR is frequently adopted in applied domains: it gives doctoral researchers a legitimate way to “build something” for a meaningful problem without losing academic credibility [12,13,28].

For doctoral training, one of the most practical benefits of DSR is that it matches how applied doctoral projects unfold. Many doctoral students do not start with a fully stable research question, evaluation strategy, and contribution claim; instead, these elements become clearer as the student encounters constraints, feedback, and new insights during design and testing. DSR explicitly supports that reality by allowing iterative movement between understanding the problem, developing an artifact, and evaluating it, rather than demanding a perfectly linear trajectory [12,13,29]. Methodological guidance in DSR also offers students a usable “research roadmap,” which can reduce uncertainty and help supervision conversations stay focused on concrete progress and next steps [29,30,31].

A second strength of DSR, from a supervision standpoint, is its emphasis on the artifact as the focal point of inquiry. In practice, artifacts can anchor supervisory meetings and milestone reviews because they make doctoral work visible and discussable. Instead of speaking only in abstract terms, the student can point to a prototype, design principles, an architecture, or an evaluation plan and use it as the center of the scholarly argument [12,28,31]. This tangibility often improves supervision quality because it gives both supervisor and student a shared object for critique: what was designed, what assumptions shaped it, what evidence supports its usefulness, and how convincingly it advances knowledge [12,30].

DSR also speaks directly to the long-standing rigor-relevance tension in computing and IS. The field has repeatedly debated whether research that is methodologically strong is also practically meaningful, and whether practically useful work can be defended as rigorous scholarship [11,12]. DSR responds by requiring explicit relevance and explicit rigor. This dual requirement is helpful in supervision because it provides a shared vocabulary for judging doctoral work, what type of artifact is being produced, what constitutes credible evaluation, and how contributions should be positioned for publication [12,13,30]. For many doctoral researchers, especially those working with industry or public-sector partners, this structure reduces ambiguity around what “counts” as doctoral-level research when the work is intervention-oriented.

Just as importantly, DSR supports doctoral development beyond implementation skills. By working through design evaluation cycles, doctoral researchers repeatedly practice framing a problem, making defensible design decisions, selecting appropriate evaluation methods, interpreting results, and reflecting on limitations and broader implications [12,13,29]. Over time, this builds habits of reasoning that are difficult to teach through coursework alone, especially the ability to justify choices, learn from failure, and refine contribution claims based on evidence rather than intention [30,32]. In many DSR projects, supervision becomes an ongoing learning dialogue: the artifact evolves, evaluation improves, and the doctoral researcher’s scholarly judgment matures through repeated cycles of critique and refinement [13,29].

At the same time, DSR can raise the bar for supervision. Supervising DSR-based work often requires engagement not only with theory and method, but also with design logic, evaluation context, and stakeholder realities. This can challenge supervisory habits formed in more linear research traditions. However, it can also strengthen the relationship when supervisors use the iterative structure to create regular feedback moments and joint sense-making: What is working? What evidence is missing? What should be refined before the next cycle? [13,30]. In settings where doctoral research is embedded in organizations, hybrid forms such as Action Design Research further highlight how design and intervention can be carried out collaboratively while preserving scholarly discipline, an approach that naturally links to the action-oriented logic discussed in Section 4 [33].

Overall, the literature positions DSR not only as a research methodology but also as a supervision-friendly training framework for applied doctoral research in computing and IS. Its emphasis on iterative cycles, artifact-centered inquiry, and explicit evaluation provides supervisors and doctoral researchers with a shared structure for navigating practice-oriented problems while still producing publishable scholarly contributions [12,13,29,30,32]. Section 4 turns to Action Research, which complements DSR by foregrounding participation, reflexive learning, and the realities of doing research inside organizations and communities.

4. Action Research and Reflexive Supervision in Real-World Settings

AR has a long tradition in the social sciences and has been widely adopted in information systems and applied computing research where inquiry is closely intertwined with practice [34]. Unlike approaches that position the researcher as a detached observer, AR explicitly frames research as a cyclical process of planning, action, observation, and reflection, carried out in collaboration with practitioners and stakeholders [35]. This orientation makes AR particularly well-suited to doctoral research that unfolds inside organizations, communities, or operational environments where problems are ill-defined, evolving, and shaped by human and institutional dynamics.

From a doctoral supervision perspective, AR offers a powerful framework for learning through engagement. Doctoral researchers using AR do not simply study a setting; they participate in it, intervene within it, and reflect critically on the consequences of those interventions. This form of engagement can be transformative for doctoral learning, as it forces students to confront real constraints, negotiate multiple perspectives, and continuously reassess their assumptions about both the problem and their role as a researcher [32,34]. Supervisors, in turn, are required to guide not only methodological rigor but also ethical judgment, reflexive practice, and relationship management.

One of the defining features of AR in doctoral training is its emphasis on reflexivity. Doctoral researchers are encouraged to examine how their positionality, decisions, and interactions influence both the research process and outcomes [36,37]. This reflexive stance is particularly valuable in supervision, as it legitimizes discussion of uncertainty, failure, and learning as integral components of doctoral progress rather than signs of weakness. Supervisory conversations in AR-based projects often extend beyond technical questions to include issues of power, trust, organizational politics, and the emotional labor of research dimensions that are frequently underrepresented in traditional doctoral training models [35,38]. Recent scholarships also underscore how supervisory power, identity, and relational dynamics influence reflexive learning processes and doctoral researcher agency [2,5].

AR also reshapes the supervisory relationship itself. Because AR projects evolve through iterative cycles rather than fixed designs, supervision tends to become more dialogical and adaptive. Supervisors and doctoral researchers jointly interpret events in the field, reassess research direction, and negotiate next steps based on emerging insights [37,38]. This collaborative dynamic can strengthen supervisory relationships, but it also requires clear communication and shared expectations to ensure that participation does not compromise academic rigor or doctoral independence.

At the same time, AR presents distinct supervisory challenges. The close involvement of doctoral researchers in organizational settings can blur boundaries between research, consultancy, and activism. Supervisors must therefore support students in maintaining critical distance, documenting decisions transparently, and articulating scholarly contributions that extend beyond local problem-solving [35,36]. Ethical oversight is also more complex in AR, particularly when interventions affect people, processes, or institutional outcomes. These considerations place additional responsibility on supervisors to help doctoral researchers navigate consent, accountability, and the unintended consequences of action-oriented research.

In applied computing and IS research, AR has been especially valuable in contexts characterized by complexity and change, such as digital transformation initiatives, information system implementations, and socio-technical interventions [35,36,38]. These same characteristics are often amplified in international and study-abroad research environments, where cultural norms, institutional structures, and resource constraints further shape research practice. While these international dimensions are not unique to AR, the methodology’s emphasis on participation, reflection, and context sensitivity makes it particularly relevant for doctoral research conducted across national and organizational boundaries. These issues are examined in greater depth in Section 6, which focuses explicitly on international and study-abroad contexts as high complexity supervisory environments.

5. Integrating Design Science Research and Action Research in Doctoral Training and Supervision

Although DSR and AR are often presented as different methodological traditions, the literature increasingly shows that they can complement each other well, especially in applied doctoral research in computing and information systems [20,33]. Both approaches move beyond purely observational research by treating real problems, stakeholder engagement, and iterative learning as central to knowledge creation. When combined carefully, DSR and AR can support doctoral training that balances structured design work with reflective practice and context awareness.

From a supervision perspective, integrating DSR and AR can help address the weaknesses that arise when either approach is used alone. DSR offers a clear structure for building and evaluating artifacts, which is useful for maintaining rigor and defending a doctoral contribution [20,29]. At the same time, some research notes that DSR projects can treat the organizational setting as relatively stable or pay less attention to the social and political dynamics that shape outcomes [28,33]. AR, on the other hand, places relationships, context, and change at the center of inquiry, but it can be harder for doctoral researchers to translate local problem solving into broader academic contribution unless the work is tightly documented and theoretically grounded [34,38]. A hybrid approach allows supervisors and doctoral researchers to combine DSR’s discipline with AR’s sensitivity to practice.

One well-known pathway for bringing these approaches together is Action Design Research (ADR). ADR blends the artifact focus of DSR with the participatory cycles of AR by developing and refining artifacts through close collaboration with practitioners [33]. This is often a good fit for doctoral projects that take place inside organizations, where research questions evolve through practice and where stakeholder interaction is unavoidable [33]. For supervision, ADR can provide a credible structure for showing how design choices, field engagement, and evaluation evidence connect to theory building and academic contribution.

Integrating DSR and AR also changes how doctoral learning tends to unfold. Rather than moving neatly from proposal to data collection to analysis, many applied doctoral projects move through cycles of action, reflection, and redesign. This pattern reflects the reality of working with complex socio-technical problems and can help doctoral researchers develop adaptability, stronger problem framing, and reflective judgment over time [29,32]. In this setting, supervisors play an important role in helping students document iterations clearly, interpret results carefully, and strengthen contribution claims as evidence accumulates [21,30].

Hybrid approaches can also shape the supervision relationship itself. Because the work is iterative and often embedded in practice, supervision may involve more frequent sense making, joint reflection, and course correction than in linear research designs [34,35]. This does not remove the supervisor’s responsibility to evaluate quality and rigor, but it can shift supervision toward a more dialogical process that supports both progress and learning, especially when the research setting is complex or uncertain [6,8].

Ethics is another reason the integration matters. Both DSR and AR involve intervention, which raises questions about consent, responsibility, and unintended consequences. AR’s long-standing emphasis on reflexivity and ethical accountability can help counterbalance the momentum that sometimes comes with design-focused work, particularly when projects affect people, processes, or institutional outcomes [36,37]. For doctoral researchers, this means learning to treat ethics as ongoing work rather than a one-time approval step, and supervisors often become key guides in navigating stakeholder expectations and accountability [23,26].

Finally, combining DSR and AR can support impact beyond the boundaries of a single dissertation. When stakeholders are engaged as collaborators rather than just participants, doctoral work can contribute to organizational learning and capacity building, while still producing academic knowledge [33,36]. This raises practical questions for doctoral programs around co-supervision, assessment, and how to evaluate outcomes that include artifacts and learning processes alongside traditional publications [16,22]. These issues become even more pronounced in international and study abroad contexts, where differences in infrastructure, institutional norms, and power dynamics shape both research practice and supervision. Section 6 examines how international contexts further influence doctoral supervision, methodological choices, and researcher development. Figure 1 illustrates the integrated doctoral research ecosystem, highlighting the alignment between Design Science Research, Action Research, and Action Design Research within supervisory and ethical scaffolding.

6. International and Study-Abroad Contexts as High-Complexity Supervisory Environments

International and study-abroad contexts introduce supervisory conditions that differ substantially from domestic doctoral training environments, particularly in applied computing and cybersecurity research. In these settings, doctoral researchers are frequently embedded within live organizations undergoing digital transformation amid institutional transition, constrained resources, and evolving governance frameworks [36,37]. These environments tend to be characterized by uneven technological capacity, incomplete cybersecurity governance structures, and shifting regulatory or administrative expectations, which can make linear research planning difficult to sustain. Drawing on international doctoral training experiences in Kosovo, Senegal, Ethiopia, and across West Africa, this section examines how such settings function as high-complexity supervisory systems in which methodological flexibility and reflexive supervision are central to maintaining scholarly rigor while enabling practice-relevant contributions.

Across the cases considered, doctoral researchers were embedded in public sector and quasi-governmental institutions responding to digitization pressures while lacking mature cybersecurity policies, workforce capacity, or continuity planning. In Kosovo, doctoral research unfolded within organizations shaped by post-conflict reconstruction and rapid Information and Communications Technology (ICT) modernization. Early diagnostics commonly revealed fragmented system architectures, undocumented access privileges, and minimal business continuity and disaster recovery planning. Doctoral interventions, therefore, included baseline cybersecurity assessments, the development of business continuity and disaster recovery documentation, the definition of access control policies, and the delivery of foundational cyber awareness training. These activities were implemented and refined through iterative cycles of diagnosis, action, observation, and reflection consistent with canonical Action Research practice [39]. Supervisory oversight in this context emphasized maintaining methodological coherence while allowing adaptation of controls to local regulatory capacity and workforce readiness. Candidates were often required to justify design choices in real time, balancing National Institute of Standards and Technology (NIST)-aligned best practices with organizational feasibility and sustainability considerations [40].

Comparable supervisory dynamics emerged in Senegal and Ethiopia, where public institutions and educational environments expanded ICT infrastructure without commensurate investment in cybersecurity governance or workforce development. In Senegal, applied work centered on strengthening institutional security postures through risk identification, access governance, workforce training, and policy development, in settings shaped by national reform initiatives and regional cooperation. In Ethiopia, interventions placed greater emphasis on usability, training effectiveness, and the human dimensions of cybersecurity adoption within developing institutional ecosystems, reflecting evidence that security outcomes in such contexts are shaped as much by organizational and behavioral factors as by technical controls [41]. For supervision, these contexts created a recurring need to integrate technical objectives with socio-technical realities, treating adoption constraints, usability findings, and training outcomes as analytically meaningful evidence rather than peripheral implementation issues.

At the regional level, doctoral work conducted across West Africa aligns with scholarship emphasizing persistent gaps in cybersecurity readiness within local governments, particularly as e-government and digital service delivery expand. Prior work within the Economic Community of West African States highlights recurring challenges linked to decentralized administration, limited regional coordination, and uneven institutional capacity [40]. In such settings, Action Research interventions commonly include policy analysis, workforce development activities, and the tailoring of governance frameworks to local administrative realities rather than the wholesale transplantation of external models. These conditions further complicate supervision because research questions frequently evolve in response to emergent organizational realities, requiring supervisors to guide candidates in reframing operational vulnerabilities as analytically tractable research problems while maintaining ethical integrity and methodological rigor.

Table 1. Comparative Country Matrix of International Action Research Contexts.

Dimension	Kosovo	Senegal	Ethiopia	West Africa (Regional)
Institutional context	Post-conflict governance, rapid ICT modernization	Public sector reform, ICT expansion	Educational and public institutions, developing infrastructure	Local governments, decentralized administration
Primary research focus	Continuity planning, access control, cyber awareness	Access governance, training, policy development	Usability, training effectiveness, adoption	Governance frameworks, capacity building
Key Action Research tasks	BCP and DR design, RBAC implementation, awareness training	Risk assessment, access policies, workforce training	Usability analysis, training evaluation, security practices	Policy analysis, workforce development, governance alignment
Supervisory complexity	High due to institutional sensitivity and sustainability concerns	High due to workforce readiness and adoption challenges	High due to usability and human factors	High due to fragmentation and regional coordination
NICE categories emphasized	Protect and Defend, Securely Provision	Securely Provision, Oversee and Govern	Operate and Maintain, Oversee and Govern	Oversee and Govern
CyberSeek pathway alignment	Incident Response, Infrastructure Support	Security Engineering, Cybersecurity Analyst	Cybersecurity Analyst, Management	Cybersecurity Management

presents a comparative country matrix of international action research contexts, highlighting differences in institutional environments, research focus, and supervisory complexity.

Methodologically, Action Research provided a supervisory structure that allowed doctoral work to remain rigorous while responsive to contextual uncertainty. Its cyclical logic enabled candidates to alternate systematically between diagnosis, intervention, observation, and reflection while remaining embedded in organizational practice [42]. In Kosovo, this structure supported the iterative development and refinement of continuity and recovery planning aligned with infrastructure constraints and governance capacity. In Senegal and Ethiopia, Action Research cycles foregrounded usability, training effectiveness, and workforce adoption alongside technical interventions, including role-based access control, basic encryption practices, and awareness training adapted to local organizational cultures [41]. In West Africa, Action Research extended more explicitly into governance and policy work, with doctoral researchers collaborating with local government stakeholders to contextualize cybersecurity frameworks within regional administrative realities [40]. Across contexts, supervisory engagement emphasized that each cycle should be theoretically informed, systematically documented, and analytically evaluated, reinforcing the legitimacy of practice-based doctoral research in applied computing.

From a supervisory standpoint, these environments required a reflexive posture that emphasized facilitation rather than prescription. Supervisors supported doctoral researchers in making defensible decisions under uncertainty, particularly when technical best practices conflicted with organizational feasibility or cultural acceptance. For example, controls that were impractical due to infrastructure limitations were treated as analytically meaningful findings when documented transparently and evaluated in relation to local constraints rather than being framed as implementation failures. Supervisory practice, therefore, extended beyond methodological coaching to include mentoring in stakeholder engagement, ethical judgment, decision making under operational risk, and reflective documentation of practice-based outcomes [37]. These supervisory demands are amplified in international environments where trust, power relations, and institutional sensitivities can shape access, participation, and the sustainability of interventions. Emerging research on intercultural supervision and multilingual doctoral environments further demonstrates how supervision practices must adapt to linguistic diversity and sociocultural variation [6,9].

Finally, international contexts provide a useful lens for examining doctoral competency development through recognized workforce frameworks. Across Kosovo, Senegal, Ethiopia, and West Africa, the applied tasks performed align with NICE Workforce Framework categories and roles [43]. Business continuity and disaster recovery planning, access control implementation, and an incident-oriented preparedness map to roles spanning Protect and Defend, Securely Provision, and Operate and Maintain. Governance and policy-focused interventions align strongly with Oversee and Govern, reflecting competencies in program management, workforce development, and institutional coordination [43]. CyberSeek pathways offer an additional interpretive frame for how these experiences translate into career-relevant capability development, with doctoral researchers building skills aligned with Cybersecurity Analyst, Incident Responder, Security Engineer, and Cybersecurity Management trajectories through sustained exposure to real operational constraints and tradeoffs [44]. Cross-border and interdisciplinary co-supervision arrangements introduce additional complexity, requiring negotiation of epistemological traditions, institutional expectations, and relational trust across disciplinary and national boundaries [5,6].

7. Supervisory Practices and Program Considerations

The preceding discussion suggests that applied doctoral research in computing and information systems increasingly takes place in settings marked by uncertainty, institutional complexity, and evolving problem definitions. These conditions shape doctoral supervision and program design in ways that differ from supervisory models built around stable research plans, clearly bounded problem spaces, and linear progress through fixed milestones [11,16]. In international and study-abroad environments, these dynamics are often intensified by uneven technological capacity, shifting governance structures, and the practical realities of conducting research across cultural and institutional boundaries [37,43].

A central consideration concerns the supervisory role. Across applied and international contexts, effective supervision is frequently characterized less by directive oversight and more by reflexive facilitation, where supervisors support iterative sense-making, ethical judgment, and adaptive decision-making under conditions of uncertainty. This framing is consistent with scholarship that treats supervision as a pedagogical relationship through which doctoral researchers acquire research practices, academic norms, and scholarly identity [23,45,46,47]. In practice-oriented doctoral work, supervisors may need to engage directly with evolving organizational constraints, stakeholder expectations, and methodological trade-offs, legitimizing iteration and learning rather than interpreting deviation from initial plans as a loss of rigor [13,48,49,50,51].

Program structures represent a second consideration. Milestone systems organized around fixed proposal approval, linear data collection phases, and narrowly specified evaluation criteria may be poorly aligned with applied research trajectories shaped by intervention and feedback cycles. The literature increasingly recognizes that doctoral training in applied domains benefits from program designs that accommodate revision, iteration, and refinement as normal features of progress [22,42,52,53,54,55]. Design Science Research and Action Research provide recognized structures through which such flexibility can be formally supported while sustaining expectations of methodological transparency, theoretical grounding, and evaluative rigor [20,29].

Assessment practices are also implicated. In applied doctoral work, scholarly contributions may be distributed across artifacts, documented interventions, evaluative analyses, and reflective accounts of practice. Assessment frameworks that prioritize only conventional empirical outputs can undervalue important dimensions of doctoral learning and practice-based contribution [31,51]. A recurring theme in the DSR and AR literatures is that explicit attention to artifact design rationales, iterative learning cycles, and evidence-based evaluation can strengthen transparency and accountability in doctoral progress assessment [21]. Such orientations are compatible with broader developments in doctoral education that emphasize competencies, outcomes, and the quality of learning processes alongside traditional outputs [56]. Recent analyses also explore how generative artificial intelligence tools are reshaping supervisory feedback processes, doctoral confirmation practices, and mentoring models, raising both opportunities and ethical considerations for supervision design [7,8,57].

The growing presence of generative artificial intelligence tools also introduces new considerations for doctoral supervision and evaluation. While such systems can assist with literature synthesis, coding support, and feedback generation, their use raises questions regarding authorship, originality, and the interpretation of research contributions. Supervisors increasingly play a critical role in guiding responsible AI use, ensuring transparency in methodological decisions, and helping doctoral researchers reflect on how AI-supported processes influence research design, evaluation practices, and scholarly integrity within applied and international research environments.

International and study-abroad programs introduce additional supervisory and programmatic considerations related to ethical oversight, cultural mediation, and stakeholder engagement. International research is frequently shaped by power asymmetries, differences in institutional norms, and infrastructural constraints that influence what kinds of interventions are feasible and what forms of evidence can be produced [48]. In these contexts, partnership arrangements and supervisory support structures become central, particularly where doctoral researchers must negotiate access, sustain trust, and document decisions under operational constraints. The literature reviewed here underscores the importance of aligning international placements and collaborations with supervisory capacity, methodological fit, and clearly articulated expectations regarding learning, impact, and scholarly contribution [49,58].

8. Methodological Fit and Use Considerations

The literature reviewed in this entry highlights how Design Science Research and Action Research can function not only as research methodologies but also as supervision- supportive frameworks for doctoral training in applied computing and information systems. Although both approaches are well established within information systems scholarship, they are less commonly discussed in terms of how they scaffold doctoral learning and supervision, particularly in international or high-complexity environments [20,33,45,46,47,59,60].

Design Science Research provides a structured approach for producing knowledge that is both rigorous and usable by linking problem relevance, artifact development, and evaluation to scholarly contribution. As a training framework, DSR helps normalize the reality that applied doctoral projects often begin with partially specified research questions, evolving contribution claims, and emergent evaluation strategies. Iteration between problem understanding, artifact development, and evaluation allows doctoral researchers to refine research direction over time while maintaining explicit expectations for grounding and evidence [21,29]. This fit becomes particularly salient in international settings where constraints related to infrastructure, governance maturity, and workforce readiness can limit the feasibility of fully specified designs at early stages, requiring doctoral researchers to justify and adapt design decisions as contextual understanding develops [36].

Action Research offers complementary strengths by foregrounding participation, reflexivity, and ethical accountability in settings where doctoral researchers are embedded in operational organizations or communities. The cyclical logic of planning, action, observation, and reflection provides a defensible structure for practice-based inquiry and supports experiential learning when interventions are systematically documented and analytically examined [39]. From a supervision standpoint, AR can help maintain scholarly coherence in environments where research questions evolve in response to stakeholder needs and where the doctoral researcher’s role necessarily involves engagement, negotiation, and relationship management [35].

The literature also emphasizes the value of integrating DSR and AR, most visibly through Action Design Research, in applied doctoral work conducted in complex organizational settings. By combining artifact-centered rigor with sensitivity to organizational dynamics and participatory engagement, ADR-style approaches support iterative design and evaluation while making contextual change and stakeholder collaboration part of the research logic rather than external complications [33,60]. In doctoral supervision, this integration can serve as a pedagogical scaffold by providing a shared structure for planning iterative cycles, documenting decisions, and strengthening contribution claims as evidence accumulates across cycles [30].

At the same time, DSR- and AR-oriented approaches are context-sensitive and not universally applicable. Their effectiveness as supervisory frameworks depends on boundary conditions such as supervisory expertise, institutional tolerance for methodological flexibility, and the ethical complexity of the research setting. Participatory and intervention- oriented work can impose substantial cognitive and emotional demands on doctoral researchers, particularly in resource-constrained or culturally complex environments, making supervisory support and institutional safeguards especially important [43]. These approaches may also be more difficult to implement in contexts where organizational access is tightly restricted, where evaluative data cannot be collected ethically or feasibly, or where doctoral programs enforce rigid methodological templates that limit iteration and adaptation [48]. Contemporary scholarship increasingly frames supervision as a relational and context-sensitive practice that must balance methodological rigor with human-centered engagement, identity development, and ethical reflexivity [2,25].

Taken together, the literature positions Design Science Research, Action Research, and their integration as powerful but context-dependent frameworks for doctoral supervision in applied computing and information systems. Their value lies in enabling methodological alignment with research context and doctoral learning objectives, while providing supervisors and doctoral researchers with explicit structures for iteration, documentation, evaluation, and reflexive practice [20,39].

9. Conclusions

Doctoral supervision in computing and information systems is increasingly shaped by expectations that doctoral researchers will produce scholarship that is both rigorous and demonstrably relevant to real-world problems. In applied domains such as cybersecurity and socio-technical system design, these expectations challenge supervisory practices and program structures that were historically optimized for stable research designs and linear progression. The literature synthesized in this entry highlights that Design Science Research and Action Research offer supervision-supportive frameworks for navigating these conditions by legitimizing intervention, iteration, and structured reflection while maintaining expectations of grounding and evidence.

International and study-abroad contexts further amplify supervisory complexity through infrastructural constraints, evolving governance arrangements, and cross-cultural dynamics that shape what interventions are feasible and what forms of evidence can be generated. In such environments, supervisory effectiveness often depends on reflexive facilitation, ethical attentiveness, and the capacity to support doctoral researchers in making defensible methodological trade-offs under uncertainty. These settings also underscore the importance of aligning partnership design and institutional support with methodological fit and supervisory capacity, particularly where doctoral work requires sustained stakeholder engagement and context-sensitive adaptation.

Looking ahead, several priorities emerge for strengthening doctoral training and supervision in applied and international research environments. Comparative and longitudinal research can clarify doctoral outcomes under DSR-, AR-, and hybrid supervision configurations, including how these approaches shape completion trajectories, publication patterns, and researcher development across institutional contexts. Further work is also needed on assessment approaches that can credibly evaluate practice-based doctoral contributions, including artifacts, documented intervention cycles, and reflective analyses, without reducing scholarly quality to narrowly defined output types. Finally, as applied cybersecurity research increasingly intersects with workforce development priorities, additional scholarship can clarify how doctoral programs may draw on role frameworks and pathway models while preserving the distinctive aims of doctoral-level inquiry and knowledge production.