Abstract
This thesis investigates how platform-dependent entrepreneurs (PDEs) develop adaptive capabilities through resourcing processes within the context of digital platform dependence. While digital platforms enable entrepreneurial success, they also introduce significant challenges such as the “lock-in” effect, resource dependence and power asymmetries because of platform governance, and intense competitive pressures, which creates an environment that is simultaneously opportunity-rich and fraught with uncertainty. Existing research often overlooks the continuous, processual nature of adaptive capability development, particularly for resource-scarce PDEs who rely on readily available, often “low-tech” digital tools. Addressing this gap, this study adopts a resourcing perspective to explore the dynamic, socially embedded practices through which PDEs build and enhance their adaptive capabilities.To address the central research question, this study employs a qualitative methodology, conducting 66 interviews with founders, managers, and employees of Chinese PDEs operating on Taobao, Alibaba, and Amazon, supplemented by secondary data such as performance indicators and operational regulations. Following an inductive coding process, the research develops a process model that details how PDEs cultivate adaptive capabilities through their resourcing activities on digital platforms.
The findings reveal a dynamic resourcing process. The first mechanism, orchestrating resource ecologies through multi-source learning, captures how PDEs proactively seek for, evaluate, and identify their resource ecologies by foraging for ecosystemic resources and triangulating multi-source information. Second, entrepreneurs engage in circumventing resource access constraints by signaling platform commitment through seller profile cultivation, exploiting mutualistic networks with external collaborators, and diversifying their engagements across complementary and substitutive platforms. The third mechanism, mobilizing digital resources for situated needs, involves digital resource-driven experimenting, collective sensemaking of platform dynamics, and customizing platform functionalities via user-driven modifications to align generic tools with specific operational requirements. This entire resourcing process is shaped by platform conditions and, in turn, enhances PDEs’ adaptive capabilities through a persistent cycle of organizational learning, collective sensemaking, and iterative adjustments.
This research offers a threefold theoretical contribution. First, this research develops a process model detailing how PDEs cultivate adaptive capabilities through resourcing on digital platforms, reframing resourcing as an inherently social practice. Specifically, this model advances the resource search strand by introducing strategic filtration, reconceptualizes resource access as a process of navigating stratified access constraints and enhances resource mobilization as a process of navigating contested schemas. Second, this research advances the understanding of adaptive capabilities as continuous, emergent competencies, offering a more dynamic and processual perspective than existing research that has predominantly focused on discrete, episodic influences. Third, the research contributes to the scholarship on digital platforms by offering nuanced insights into the diverse strategies PDEs employ to manage platform dependence, particularly in low-tech environments. This research reveals that PDEs frequently deploy “low-tech” or non-digital strategies rooted in organizational and relational approaches, providing a more holistic understanding of entrepreneurial agency in complex platform-based contexts. For practical implications, this research offers actionable insights for PDEs to navigate resource dependence and for platform owners and policymakers aiming to foster healthier, more sustainable entrepreneurial ecosystems.
| Date of Award | 15 Nov 2025 |
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| Original language | English |
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| Supervisor | Jun Luo (Supervisor), Russa Yuan (Supervisor) & Yangyang Jiang (Supervisor) |