A note on presuppositions of social science in the context of doing MBA, Housing Studies and Accounting & Finance dissertation projects

A note on presuppositions of social science in the context of doing MBA, Housing Studies and Accounting & Finance dissertation projects (information from perplexity.ai)

My question 1: "In the context of doing MBA, Housing Studies and Accounting & Finance dissertation projects, are there any presuppositions social science? And if, so what are the strengths and limitations of generating valid and practical research findings from dissertation projects that rely on these presuppositions?"

The reply: "Social science research, including in MBA, Housing Studies, and Accounting & Finance dissertations, relies on philosophical presuppositions known as ontology (nature of reality) and epistemology (how knowledge is gained). These form the basis of paradigms like positivism, interpretivism, pragmatism, and critical realism, which your past projects have explored, such as critical realism for causal mechanisms in housing policy and MBA leadership themes.

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Key Presuppositions

Ontology assumes reality is either objective and independent (realism in positivism/critical realism) or subjective and constructed (relativism in interpretivism). Epistemology posits knowledge comes from empirical observation and testing (positivism) or interpretive understanding of meanings (interpretivism/pragmatism). In your fields, positivism suits quantitative finance analysis, while critical realism fits Housing Studies' structural mechanisms like policy impacts on homeownership confidence.

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Strengths for Valid Findings

Positivism yields reliable, generalizable results through rigorous testing, ideal for causal predictions in Accounting & Finance dissertations. Pragmatism and critical realism enhance practicality by mixing methods for real-world applicability, such as hybrid surveys and interviews in MBA/Housing projects, producing actionable insights on stakeholder concerns. Interpretivism uncovers nuanced meanings, strengthening validity in context-specific studies like Hong Kong housing perceptions.

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Limitations for Practical Findings

Positivism overlooks subjective experiences, limiting depth in complex social phenomena like organizational behavior in MBA research. Interpretivism risks bias and poor generalizability, challenging scalability for finance trends. Pragmatism may sacrifice rigor for "what works," while critical realism demands time-intensive mechanism exploration, straining 4-month part-time timelines without yielding quick practical outputs."

My question 2: "Are the presuppositions of natural science and that of social science different?"

The reply: "Yes, the presuppositions of natural science and social science differ significantly, primarily in ontology (nature of reality) and epistemology (nature of knowledge). Natural science assumes an objective, stable reality governed by universal laws, while social science grapples with subjective, context-dependent human behaviors and meanings.

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Ontological Differences

Natural science presupposes a single, mind-independent reality where phenomena like gravity operate consistently regardless of observers. Social science views reality as layered or constructed—stratified into empirical (observable), actual (events), and real (underlying mechanisms) domains, as in critical realism favored in your Housing Studies work. This allows social research to address human agency and structures, unlike natural science's focus on inert matter.

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Epistemological Differences

Natural sciences prioritize objective measurement and controlled experiments for value-neutral laws, enabling high reproducibility. Social sciences incorporate subjectivity, using interpretation alongside empiricism to understand intentions and contexts, which suits MBA/Accounting dissertations on behaviors like investor confidence. Both share empiricism and skepticism, but social science demands reflexivity to mitigate researcher bias.

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Strengths and Limitations

Natural science presuppositions yield precise predictions but struggle with human variability, limiting direct application to fields like Housing Studies policy impacts. Social science approaches generate contextually valid insights for practical dissertation outcomes, though they risk lower generalizability compared to natural science universality. In your projects, blending them (e.g., quantitative finance models with qualitative housing narratives) leverages complementary strengths."

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