The Power of Critical Thinking in Doctoral Programmes: Creating New Knowledge

Mitesh Jain upGrad International Josse Roussel, PhD, HDR Alan O'Neill Dr. Paul Davis Brian Fitzgerald Derya Yalimcan Mahzad Sareer Killian Chute

Introduction

The transition from a master’s to a doctoral programme marks a significant shift from learning existing knowledge to creating new knowledge. This journey is particularly crucial in programmes like the Doctor of Business Administration (DBA) and the Doctor of Education (Ed.D.), where practical application and academic research converge. Critical thinking is essential in this process, enabling doctoral students to analyse, evaluate, and synthesise information to generate original insights. This article explores how critical thinking is applied in analysis and writing within doctoral programmes, drawing on my experiences from completing the thesis phases in both my Ph.D. and DBA.

Understanding Critical Thinking in Academic Research

Critical thinking in academic research involves questioning assumptions, evaluating evidence, and synthesising information to form well-reasoned conclusions. It goes beyond merely describing what others have done to critically analysing their work and constructing new arguments. Brookfield (2012) emphasises the importance of critical thinking in enabling students to contribute original knowledge to their fields.

In master’s programmes, such as MBA courses, the focus is primarily on learning and applying existing knowledge. Students are assessed on their understanding and application of this knowledge through exams, essays, and projects. In contrast, doctoral programmes require students to create new knowledge. This involves identifying gaps in existing research, proposing new hypotheses, and testing these through rigorous methodologies. Critical thinking is essential in this process, as it allows students to develop innovative solutions to complex problems, as highlighted by Brookfield (2012).

Developing a Critical Mindset

Developing a critical mindset is the first step towards effective critical thinking. This involves questioning assumptions, identifying biases, and considering alternative perspectives. During my Ph.D. journey, which focused on viewing the organisation as a living system rather than a machine, I consistently challenged existing assumptions about organisational behaviour. For instance, I explored how organisations function as complex adaptive systems, questioning the traditional mechanistic view that treats organisations as fixed entities with predictable behaviours. This perspective is supported by Stacey (2011), who argues that organisations behave more like living systems, constantly evolving and adapting to changes. Conversely, Snowden and Boone (2007) present a more static view, which I critiqued for its methodological flaws.

Engaging deeply with literature is also vital. This means reading widely and critically, identifying gaps in research, questioning the validity of findings, and considering their implications. During my DBA programme, which explored the impact of generative AI on organisational efficiency, I adopted a structured approach to reading and annotating research papers, categorising my notes under headings such as purpose, findings, methods, recommendations, strengths, and weaknesses. This structured approach enabled me to critically evaluate each paper and understand its contribution to my research. For example, in a recent study, Makridakis et al. (2023) highlight discrepancies and areas lacking rigorous analysis in various studies on AI integration, which informed my own research direction.

Structuring Annotations for Critical Analysis

Effective annotation of research papers is a critical skill for doctoral students. By categorising notes under headings such as the purpose of the research, findings, methods, recommendations, strengths, and weaknesses, students can better evaluate each paper’s contribution to their own research. Tranfield, Denyer, and Smart (2003) suggest that this structured approach allows for quick referencing and integration of important points into one’s own research. During my DBA programme, I used this approach to annotate key papers on AI and business strategy, which helped me identify and integrate crucial insights.

This method also helps identify patterns and trends in the literature. By comparing notes across multiple papers, students can identify common themes and gaps in the research, refining their research questions and hypotheses. During my Ph.D. research, I used this approach to identify gaps in the literature on organisational theories. Specifically, I examined how different authors approached the concept of organisations as living systems versus machine-like entities. Wheatley (2006) supports the living systems view, arguing for the importance of adaptability and learning, whereas Morgan (2006) maintains a more static view. By critiquing these perspectives and highlighting their methodological flaws, I proposed a more nuanced understanding of organisational dynamics.

Critical Analysis of Literature Reviews

A literature review in a doctoral thesis goes beyond summarising existing research. It involves synthesising multiple studies to identify patterns, contradictions, and gaps. This synthesis forms the basis for developing new hypotheses and research questions. In my Ph.D. research, I conducted a comprehensive literature review on organisational behaviour, identifying a gap regarding the impact of viewing organisations as living systems on their adaptability and resilience. Holland (1995) discusses how organisations, much like biological entities, evolve in response to environmental pressures. By contrasting these findings with those of more traditional, mechanistic views, I argued for the superior adaptability of living system-based organisational models.

Critical analysis of literature reviews also involves evaluating the quality of the research. This includes assessing the validity and reliability of the findings, the robustness of the methodologies, and the relevance of the research to your own work. During my DBA programme, I critically evaluated the literature on the impact of generative AI on organisational efficiency, identifying methodological weaknesses such as small sample sizes or lack of longitudinal data. This critical analysis, as highlighted by Brynjolfsson and McAfee (2022), informed the design of my own research and allowed me to propose more robust methodologies for future studies.

Critical Thinking in Research Design

Critical thinking is crucial in the design phase of research. Formulating research questions and hypotheses requires a deep understanding of existing knowledge and a clear vision of how to advance it. During my DBA, I carefully designed my research to address specific gaps in the literature on AI’s impact on organisational efficiency, critically evaluating different methodologies to ensure robustness and meaningful results (Creswell & Plano Clark, 2017). For instance, I compared qualitative and quantitative approaches, ultimately deciding on a mixed-methods strategy to capture the multifaceted impact of AI technologies.

Critical thinking in research design also involves anticipating potential challenges and limitations. Maxwell (2013) highlights the importance of considering potential biases and limitations, which guided my approach in developing mitigation strategies. During my Ph.D. research, I anticipated challenges related to data collection in studying organisations as living systems and developed strategies such as using multiple data sources to ensure the robustness of my research.

Conducting Critical Interviews and Workshops

Conducting interviews and workshops is a common method in qualitative research. To gather rich and insightful data, it is essential to guide interviewees to think deeply about their responses. Yin (2018) emphasises the importance of asking probing questions and encouraging participants to consider multiple perspectives. In my DBA research, I conducted interviews with business leaders to explore the impact of generative AI on organisational efficiency. Using critical questioning techniques, I was able to uncover underlying issues and gain a deeper understanding of their experiences and challenges with AI integration.

Critical thinking is also important in analysing qualitative data. Braun and Clarke (2006) discuss the significance of looking for patterns and themes in the data while considering the context and nuances of the responses. During my Ph.D. research, I conducted workshops with employees to explore their perceptions of their organisation as a living system. By using a critical lens to analyse the data, I identified themes related to adaptability and resilience, contrasting these with the more rigid structures described in traditional organisational models. This approach helped develop a deeper understanding of the dynamic interactions within the organisation.

Comparative Analysis of Research Findings

Comparative analysis involves evaluating and contrasting different studies to build strong, evidence-based arguments. By comparing findings from various sources, doctoral students can develop a comprehensive view of their research topic. In my Ph.D. thesis, I compared studies on organisational theories, identifying common challenges and unique solutions, which I integrated into my research framework. Morgan (2006) and Senge (1990) provide differing conclusions on organisational learning and adaptability, which I used to strengthen my argument for viewing organisations as living systems.

Comparative analysis also helps identify gaps in the literature. By comparing findings from different studies, students can identify areas where there is a lack of research or inconsistent findings, refining research questions and hypotheses. During my DBA programme, I used comparative analysis to identify gaps in the literature on AI’s efficiency gains. Bessen (2022) points out gaps regarding the long-term impacts on employee productivity and organisational culture, which became a focal point of my research.

Critical Writing Techniques

Writing critically involves constructing logical and coherent arguments, critiquing existing work, and supporting your arguments with evidence. Hart (2018) emphasises that this is a key skill for doctoral students, who are expected to contribute original knowledge to their fields. During my Ph.D. and DBA programmes, I developed my critical writing skills by constructing logical and coherent arguments, critiquing existing work, and supporting my arguments with evidence.

Critical writing also involves synthesising multiple sources to develop a comprehensive view of the research topic. This means not only describing what others have found but also analysing and evaluating their work to develop your own arguments. During my Ph.D. research, I synthesised multiple sources to develop a comprehensive view of the impact of viewing organisations as living systems. This synthesis helped build strong, evidence-based arguments that contributed original insights to my field.

Recursive Analysis and Writing

Analysing research results in a recursive manner—continually re-evaluating findings with a critical lens—is a hallmark of doctoral work. Thomas and Harden (2008) highlight that students must constantly question their conclusions, compare them with existing studies, and refine their arguments. During my DBA programme, I used recursive analysis to continually re-evaluate my findings on AI’s impact, refining my arguments and developing deeper insights.

Recursive analysis and writing also involve using feedback to enhance critical analysis. Maxwell (2013) notes the importance of considering feedback from supervisors and peers to refine your arguments and strengthen your research. During my Ph.D. research, I used feedback to continually re-evaluate my findings on organisational behaviour, developing a deeper understanding of the concept of organisations as living systems.

Critical Evaluation of Industry Reports and Company Documents

Assessing the credibility and relevance of industry data is crucial for doctoral research. Brynjolfsson and McAfee (2022) emphasise the importance of integrating practical insights into academic research to bridge the gap between theory and practice. During my DBA research, I critically evaluated industry reports and company documents to gather practical insights into the impact of generative AI on organisational efficiency, developing a comprehensive view of the research topic and building strong, evidence-based arguments.

Critical evaluation also involves considering the strengths and weaknesses of the data sources. Briner and Denyer (2012) highlight the need to assess the credibility and reliability of the data and consider how it contributes to your research. During my Ph.D. research, I critically evaluated industry reports to gather insights into viewing organisations as living systems, developing a comprehensive view of the research topic and building strong, evidence-based arguments.

Constructing and Supporting Arguments

Building evidence-based arguments is a key skill for doctoral students. Hart (2018) discusses the importance of using critical thinking to develop logical and coherent arguments and supporting them with evidence. During my Ph.D. and DBA programmes, I developed skills in constructing and supporting arguments by using critical thinking to develop logical arguments and supporting them with evidence.

Critical thinking also involves addressing counterarguments and opposing views. Creswell and Plano Clark (2017) highlight the importance of considering alternative perspectives and using them to strengthen your arguments. During my Ph.D. research, I addressed counterarguments to develop a comprehensive view of the impact of viewing organisations as living systems, building strong, evidence-based arguments.

Critical Thinking in Data Analysis

Interpreting quantitative and qualitative data critically is essential for doctoral research. Yin (2018) discusses the importance of recognising patterns and anomalies in the data and drawing valid conclusions based on critical data analysis. During my DBA research, I used critical thinking to interpret quantitative data on the impact of generative AI, developing a deeper understanding of its multifaceted effects.

Critical thinking also involves considering the context and nuances of the data. Braun and Clarke (2006) highlight the importance of looking beyond surface-level findings to understand deeper implications. During my Ph.D. research, I used critical thinking to interpret qualitative data on the concept of organisations as living systems, developing a deeper understanding of dynamic interactions and their impact on performance.

Synthesising Research for New Knowledge Creation

Integrating findings from diverse sources is a key skill for doctoral students. Hart (2018) emphasises the importance of synthesising multiple studies to develop new theories and models. During my Ph.D. and DBA programmes, I synthesised research findings from diverse sources to develop new theories and models, contributing original insights to my fields.

Synthesising research also involves formulating new hypotheses and research questions. Maxwell (2013) highlights the importance of identifying gaps in the literature and developing new ideas to address these gaps. During my Ph.D. research, I formulated new hypotheses to address gaps in the literature on viewing organisations as living systems, developing new theories and models that contributed original insights.

Conclusion

Transitioning from a master’s to a doctorate programme involves shifting from learning and applying existing knowledge to generating and critiquing new knowledge. This requires a move from descriptive to critical writing, structured annotation of research, and a rigorous, recursive approach to research and analysis. By mastering these skills, doctoral students become creators of new knowledge, contributing original thought supported by both academic and industry evidence. Through my experiences in both my Ph.D. and DBA programmes, I have learnt the importance of critical thinking in analysis and writing. This has enabled me to contribute original insights to my fields and develop a deeper understanding of the research topics I have explored.

References

Bessen, J. (2018, January 19). AI and Jobs: the role of demand. National Bureau of Economic Research Working Paper Series. https://www.nber.org/papers/w24235

Bonetti, L. (1995). (Helix Books) John Holland Hidden Order How Adaptation Builds Complexity (Helix Books) Basic Books (1996). Www.academia.edu. https://www.academia.edu/92723469/_Helix_Books_John_Holland_Hidden_Order_How_Adaptation_Builds_Complexity_Helix_Books_Basic_Books_1996_

Braun, V., & Clarke, V. (2006). Using thematic analysis in psychology. Qualitative Research in Psychology, 3(2), 77–101. https://doi.org/10.1191/1478088706qp063oa

Briner, R. (2023). Systematic Review and Evidence Synthesis as a Practice and Scholarship Tool . Oup.com. https://academic.oup.com/edited-volume/36314/chapter/318650175

Brookfield, S. D. (2011). Teaching for Critical Thinking: Tools and Techniques to Help Students Question Their Assumptions. In Google Books. John Wiley & Sons. https://books.google.com/books?hl=en&lr=&id=r7DG2e41pZMC&oi=fnd&pg=PP1&dq=Brookfield

Brynjolfsson, E., & McAfee, A. (2014). The Second Machine Age: Work, Progress, and Prosperity in a Time of Brilliant Technologies. In Google Books. W. W. Norton & Company. https://books.google.com/books?hl=en&lr=&id=WiKwAgAAQBAJ&oi=fnd&pg=PA1&dq=Brynjolfsson

Hart, C. (2018). Doing a Literature Review : Releasing the Research Imagination. Doing a Literature Review, 1–352. https://www.torrossa.com/gs/resourceProxy?an=5019541&publisher=FZ7200

Maxwell, J. A. (2012). Qualitative Research Design: An Interactive Approach. In Google Books. SAGE Publications. https://books.google.com/books?hl=en&lr=&id=xAHCOmtAZd0C&oi=fnd&pg=PR5&dq=Maxwell

Morgan, G. (2021, October 22). Images of Organization. SAGE Publications Ltd. https://uk.sagepub.com/en-gb/eur/images-of-organization/book229704

Nikolopoulos, K., Punia, S., Sch?fers, A., Tsinopoulos, C., & Vasilakis, C. (2020). Forecasting and Planning during a pandemic: COVID-19 Growth rates, Supply Chain disruptions, and Governmental Decisions. European Journal of Operational Research, 290(1). https://doi.org/10.1016/j.ejor.2020.08.001

Plano Clark, V. L. (2016). Mixed Methods Research. The Journal of Positive Psychology, 12(3), 305–306. https://doi.org/10.1080/17439760.2016.1262619

Snowden, D., & Boone, M. (2007, November). A Leader’s Framework for Decision Making. Harvard Business Review. https://hbr.org/2007/11/a-leaders-framework-for-decision-making

Stacey, R. D. (2007). Strategic Management and Organisational Dynamics: The Challenge of Complexity to Ways of Thinking about Organisations. In Google Books. Financial Times Prentice Hall. https://books.google.com/books?hl=en&lr=&id=PENbGZDXTU4C&oi=fnd&pg=PR12&dq=Stacey

Thomas, J., & Harden, A. (2008). Methods for the Thematic Synthesis of Qualitative Research in Systematic Reviews. BMC Medical Research Methodology, 8(1), 1–10. Biomedcentral. https://doi.org/10.1186/1471-2288-8-45

Tranfield, D., Denyer, D., & Smart, P. (2003). Towards a Methodology for Developing Evidence-Informed Management Knowledge by Means of Systematic Review. British Journal of Management, 14(3), 207–222. https://doi.org/10.1111/1467-8551.00375

Yin, R. K. (2018, May 10). Case Study Research and Applications. SAGE Publications Inc. https://us.sagepub.com/en-us/nam/case-study-research-and-applications/book250150