Discerning promising practices: innovation in high growth firms
    
    Walpole, Gary, Li, Songdi, Clifton, Nick and Liu, Zheng ORCID: https://orcid.org/0000-0001-7240-3501
  
(2025)
Discerning promising practices: innovation in high growth firms.
    International Journal of Entrepreneurial Behavior and Research (IJEBR).
    
     ISSN 1355-2554 (Print), 1758-6534 (Online)
  
   (In Press)
	 (doi:10.1108/IJEBR-04-2024-0389)
  
| ![Author's Accepted Manuscript [thumbnail of Author's Accepted Manuscript]](https://gala.gre.ac.uk/style/images/fileicons/application_pdf.png) | PDF (Author's Accepted Manuscript) 51206 LIU_Discerning_Promising_Practices_Innovation_In_High_Growth_Firms_(AAM)_2025.pdf - Accepted Version Restricted to Repository staff only Available under License Creative Commons Attribution Non-commercial. Download (348kB) | Request a copy | 
Abstract
Purpose
While innovation theories are often applied within the context of non-small- and medium-sized enterprises (SMEs), more research is needed to understand SMEs and in particular high-growth firms (HGFs) among the SMEs, known as high-growth SMEs (HGSMEs). This study provides empirical insights on the innovation determinant factors, processes and outcomes employed by HGSMEs based in Wales, UK. It answers the research question of “How have HGSME leaders developed and implemented effective innovation practices?”
Design/methodology/approach
The study adopted a multiple case study methodology with two stages. Stage one included a pre-interview data-gathering tool to capture innovation processes of eight purposively selected HGSMEs in Wales, UK regarding their employee engagement, continuous improvement practices and innovation methods/tools. Stage two involved semi-structured interviews with 19 leaders within these HGSMEs to examine in-depth details on their innovation processes and practices. Data were analysed using content analysis, interpretative phenomenological analysis and triangulation data methods.
Findings
Drawing upon quantitative and qualitative data analysis, four main innovation practices were identified. First, leaders created an innovation culture emphasising problem-solving. Second, innovation processes engaged workers with the productivity and innovation challenges of the business, whilst distributing problem-solving tasks. Third, formal and informal mechanisms captured performance data and facilitated feedback. Fourth, strong supplier/customer relationships enabled innovation implementation. A theoretical framework is developed to demonstrate the innovation processes and practices of HGSMEs.
Practical implications
The paper identifies successful practices that could be adopted widely, beyond firms in Wales, to enhance innovation performance of HGFs. It informs regional and national policymakers' context-specific models of innovation that could guide their focus and incentive schemes as well as SME leaders by sharing these successful cases and their salient practices.
Originality/value
This study advances the nuanced and comprehensive understanding of innovation in HGSMEs. Based on empirical evidence, findings connect leadership and innovation and develop a framework of context-specific HGSME innovation factors, processes and their interaction, which is novel in the current literature.
| Item Type: | Article | 
|---|---|
| Uncontrolled Keywords: | innovation practices, SME leadership, high-growth firms, high-growth SMEs, Welsh enterprises, innovation culture, leading innovation | 
| Subjects: | H Social Sciences > H Social Sciences (General) H Social Sciences > HB Economic Theory H Social Sciences > HD Industries. Land use. Labor | 
| Faculty / School / Research Centre / Research Group: | Greenwich Business School Greenwich Business School > Networks and Urban Systems Centre (NUSC) Greenwich Business School > School of Business, Operations and Strategy | 
| Last Modified: | 08 Oct 2025 10:40 | 
| URI: | https://gala.gre.ac.uk/id/eprint/51206 | 
Actions (login required)
|  | View Item | 
Downloads
Downloads per month over past year
 Tools
 Tools Tools
 Tools