HACKING GROWTH, DESIGNING CHANGE: THE STRATEGIC ROLE OF GROWTH HACKING IN PLATFORM-BASED ENTERPRISES

While growth hacking (GH) has gained substantial traction in practice as a data-intensive and experimentation-driven strategy to drive scalable growth, particularly among startups and resource-constrained firms, its academic conceptualization remains fragmented, under-theorized, and disconnected from adjacent frameworks such as lean startup, agile, and design thinking. More specifically, this dissertation seeks to address these gaps through three interconnected yet distinct studies that collectively explore the theoretical grounding, practical implications, and enabling conditions of GH implementation. The main objective of this doctoral thesis is to conceptualize and investigate GH as an emerging data-driven and managerial experimental approach, and to understand its role, implementation benefits and challenges, and enabling conditions within the experimentation-based business practices context. Accordingly, Study 1 develops a conceptual framework that situates GH within the broader family of managerial experimental approaches (MEAs), such as lean startup, agile, and design thinking. It systematically maps the theoretical interconnections between MEAs and business model dynamics (BMDs), offering an integrated perspective that clarifies GH’s unique contributions. Study 2, building on the foundations of Study 1, investigates the practical benefits and challenges of GH adoption in platform-based micro, small, and medium-sized enterprises (MSMEs) through a qualitative multiple-case study, highlighting both enabling factors and barriers. Study 3, responding to the need for a more granular understanding of GH success drivers, explores the role of data-driven decision-making (DDDM) through a fuzzy-set Qualitative Comparative Analysis (fsQCA), identifying optimal configurations of organizational and technical capabilities that foster effective GH implementation. The thesis adopts a mixed-method approach: Study 1 applies a systematic literature review, mapping 51 peer-reviewed journal articles; Study 2 employs qualitative methods with in-depth interviews; and Study 3 uses configurational analysis to determine necessary and sufficient conditions for successful GH outcomes. Findings of Study 1 reveal key conceptual clusters linking MEAs and BMDs, clarifying GH’s theoretical position and its synergies with other approaches. Study 2 uncovers the practical relevance of GH for platform-based MSMEs, demonstrating its potential for scalable growth and adaptability while also identifying organizational resistance, skill gaps, and misalignment with traditional structures as major implementation challenges. Study 3 provides empirical evidence that effective GH implementation arises not from isolated capabilities but from synergistic configurations of DDDM dimensions such as learning orientation, technological infrastructure, and knowledge renewal. This thesis, through Study 1, shall contribute to the integration of GH within the MEAs and BMDs literature, advancing the conceptual clarity of this emergent approach and offering a structured research agenda for the most promising future research streams. Study 2 advances practical insights of GH implementation by mapping its 6 enablers and obstacles, offering insights for managers seeking to adopt GH in resource-constrained environments. Finally, Study 3 enhances the literature on DDDM and GH by identifying actionable capability combinations that underpin successful growth outcomes, particularly in platform-based enterprises.