Abstract:
This doctoral research presents a qualitative phenomenological investigation into the lived experiences of social entrepreneurs in Bangladesh, with a focus on how learning experiences (LE) and social capital (SC) shape their social entrepreneurship development (SED) within volatile socio-economic, political, and cultural contexts. Drawing on Verbal Protocol Analysis (VPA), unstructured “think-aloud” interviews were conducted with five nationally recognized social entrepreneurs. The narratives were transcribed verbatim and analyzed through phenomenological reduction, bracketing researcher bias to allow the essences of social entrepreneurship development (SED) to emerge authentically. Through this process, distinct essences of SED were identified, each linked to noematic shapers—classified as either learning experiences (LE) or social capital (SC)—mapping the noetic processes of conscious learning, intentional action, and reflective adaptation.
Findings demonstrate that LE and SC function in a dynamic, interdependent relationship rather than as discrete categories. Social capital—embodying networks, trust, knowledge flows, and resource access—interacts continuously with learning experiences to foster resilience, strategic adaptability, and innovation. This interplay enables social entrepreneurs to transform systemic constraints into opportunities for impact and sustainability, thereby advancing inclusive solutions to pressing social challenges.
The study contributes a novel phenomenological framework that decodes the interwoven dimensions of social entrepreneurial experience in resource-constrained environments. By centering intentional consciousness and contextual interplay, it extends theoretical understanding of social entrepreneurship while offering practical insights for strengthening ecosystem support in emerging economies. In doing so, the thesis underscores how social entrepreneurship is not a linear trajectory but an iterative process of learning, trust-building, and adaptive practice sustained amid uncertainty