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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.
- doctoral supervision
- design science research
- action research
- applied computing
- international doctoral education
- cybersecurity
- research methods
- socio-technical systems
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.
This entry is adapted from the peer-reviewed paper 10.3390/encyclopedia6030070
References
- Liu, Y.; Zhang, X.; Dai, K. Mapping the terrain of global research trajectory of international doctoral students: A bibliometric review. High. Educ. Res. Dev. 2025, 44, 480–499.
- Byrne, T.; Leggett, S.E.; Mallampooty, V.; Jameel, R.; Zapata, A.; Blodgett, P.; Wu, L.; McMillian, A.; Smithing, F.; Volny, E.; et al. The struggle for identity in doctoral supervision: A phenomenologically grounded qualitative study of power and conflict. Stud. High. Educ. 2025, 1–18.
- Moshtari, M.; Schleper, M.C. Supervisors’ power sources and doctoral students’ working conditions: A qualitative study of doctoral supervision in a developing country. Stud. High. Educ. 2025, 50, 2030–2047.
- Dai, K.; Doi, K.; Oladipo, O.A. The Motivation and Research Experiences of International Doctoral Students in China: Navigating the PhD Journey; Routledge: London, UK, 2025.
- Naidoo, R.; Samuel, M.A. Crossing borders, building bridges: A reflective case of interdisciplinary PhD co-supervision in health sciences and education. Transform. High. Educ. 2026, 11, 638.
- Xu, Y.; Liu, J. Understanding doctoral supervisors’ understanding of supervisory goals in China: A social-cultural interpretation. High. Educ. 2025, 91, 387–404.
- Jensen, L.X.; Bearman, M.; Boud, D.; Konradsen, F. Feedback encounters in doctoral supervision: The role of generative AI chatbots. Assess. Eval. High. Educ. 2025, 1–14.
- Thong, C.L.; Atallah, Z.; Islam, S.; Lim, W.; Cherukuri, A.K. AI-powered tools for doctoral supervision in higher education: A systematic review. J. Inf. Knowl. Manag. 2025, 24, 2530001.
- Liu, W.; Han, J.; Singh, M.; Wright, D. A translanguaging approach to doctoral supervision: Leveraging students’ multilingualism as intellectual resources. Int. J. Appl. Linguist. 2025, 36, 162–174.
- Abdelghaffar, A.; Eid, L. A critical look at equity in international doctoral education at a distance: A duo’s journey. Br. J. Educ. Technol. 2025, 56, 834–851.
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