Opening the black box of Growth Hacking: Insights into the microfoundations of Lean Startup Capabilities

Caterina Foggetti, Angelo Natalicchio, Lorenzo Ardito and Vito Albino

Journal of Business Research, 2025, vol. 191, issue C

Abstract: Growth hacking (GH) is a strategy of rapid experimentation and testing aimed at scaling firms’ business model. Based on the relationships established in the literature between GH and Lean Startup (LS), we acknowledge LS capabilities play a crucial role in the successful implementation of GH, yet a deeper understanding of how LS capabilities support GH is needed, especially from a theoretical perspective. In this context, through a multiple case study approach analyzing six different startups, we aim to unveil the microfoundations of LS capabilities supporting the implementation of GH, hence opening the black box of GH. This research advances the theoretical development and understanding of GH, with a focus on its relationship with LS and underlying LS capabilities, by leveraging the microfoundations perspective to reconcile theory and practice when examining GH. Additionally, it informs the debate on how GH can create favorable conditions for scaling business models.

Keywords: Growth hacking; Lean startup capabilities; Business model; Microfoundations; Entrepreneurship (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://www.sciencedirect.com/science/article/pii/S0148296325000955
Full text for ScienceDirect subscribers only

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:eee:jbrese:v:191:y:2025:i:c:s0148296325000955

DOI: 10.1016/j.jbusres.2025.115272

Access Statistics for this article

Journal of Business Research is currently edited by A. G. Woodside

More articles in Journal of Business Research from Elsevier
Bibliographic data for series maintained by Catherine Liu ().