This study explores how the COVID-19 pandemic has forced Flemish growth-oriented entrepreneurs to build entrepreneurial resilience. We rely on a research framework that consists of a “challenge-reaction-learning loop” to empirically investigate how entrepreneurial resilience is built in times of the COVID-19 crisis. To investigate this complex entrepreneurial learning process, we use data that have been collected during the first and second wave of the COVID-19 pandemic. By using several datapoints, we could identify (1) the specific challenges growth-oriented firms are facing as a result of the COVID-19 crisis; (2) how these entrepreneurs reacted to these challenges; and (3) what they learned during the first and second wave of the pandemic and how they perceive the future. By making this entrepreneurial learning process explicit and dividing it into an iterative “challenge-reaction-learning loop”, this study is relevant for all entrepreneurs, as it contains several interesting lessons learned. We also contribute to academic literature as we provide future researchers a tangible framework to further elucidate how entrepreneurial resilience is built in times of crisis.

Purpose: Few high-growth firms (HGFs) are able to maintain high-growth over time. The purpose of this paper is to find out why only a small number of firms become persistent HGFs, explicitly focusing on the role of the founding entrepreneur in this process.Design/methodology/approach: Initially, 28 semi-structured interviews were performed with high-growth entrepreneurs to discover why so few founders could become persistent high-growth entrepreneurs. In a second phase, four case studies were conducted to uncover the factors that facilitate a swift evolution from the “managerial” role to the “strategic” role.Findings: High-growth entrepreneurs, who quickly make a transition from a managerial role into a strategic role are more likely to keep their firm on its high-growth trajectory. This transition is made possible by: the early development of strategic skills; the presence of a high quality human capital base; and an organizational structure with characteristics from Mintzberg’s “machine bureaucracy.”Practical implications: The results are vital for entrepreneurs of “one-shot” HGFs with the ambition to make their firm a “persistent” HGF. If high-growth rates are to be sustained, the three factors that emerged from the authors’ analysis should foster the delegation of managerial tasks, resulting in an easier transition toward a “strategic role.”Originality/value: Insights are valuable as both founders and governmental institutions can benefit from knowing which factors contribute to a successful phase transition from “manager” to “strategist.”

Purpose: This paper aims to analyze the effect of industry growth rates on the characteristics of high-growth firms (HGFs) that are active in a particular industry. By making a distinction between HGFs active in stable and declining industries and HGFs active in growing and high-growing industries, it is analyzed if the main dimensions of firm performance are significantly different for HGFs active in one of these different industry types. Gaining more insight into this industry aspect of high firm growth is important as governmental measures towards HGFs may be more effective if they have a specific sectoral focus.Design/methodology/approach: A subset of 740 Belgian HGFs was analyzed. Data were gathered from the Belfirst database. HGFs were classified within their corresponding industry type: a declining industry (negative growth), a stable industry (0 −5% growth), a growing industry (5 −10% growth) and a high-growth industry (>10% growth). Four dimensions of structural firm performance that are expected to correlate with high growth were taken into consideration: productivity (value added per FTE), profitability (ROA), innovativeness (intangible assets) and financial health (solvency and liquidity).Tukey's range tests in conjunction with post-hoc analysis of variance (ANOVA) tests were carried out to test for significant differences in all the mentioned variables for the HGFs in the four different industry types.Findings: Results show that HGFs active in a stable industry are not significantly more profitable or innovative than HGFs active in a growth industry. However, significant differences could be encountered when it comes to the other two dimensions of structural firm performance: productivity and financial health. It is shown that HGFs active in declining and stable industries are significantly more productive than HGFs active in growth industries and high-growth industries. Also, HGFs active in declining and stable industries have significantly higher liquidity ratios than firms active in growth industries, pointing towards a better financial health for HGFs in nongrowing industries.Research limitations/implications: The results confirm the conceptual logic that the differences between resource-based view (RBV) and industrial organization (IO) propositions will have an impact on the drivers of firm performance and high business growth. Every future study that focuses on the growth determinants of HGFs should be aware that considering the subset of HGFs as one homogenous group may be suboptimal. It is likely that the growth determinants of both HGF types will indeed be fundamentally different.Originality/value: Until now, all studies on HGFs have considered the subset of HGFs as a whole. This paper tried to disentangle the subset based on the growth rate of the industry in which HGFs are mainly active. In this proposition, a reason for the lack of knowledge about characteristics of HGFs may – at least partially – be found in the fact that industry membership plays an important role in determining the characteristics of a high-growth firm. Future studies focusing on high-growth determinants may benefit from systematically taking the industry growth rates into account, with the knowledge that the propositions of two different theories – IO and RBV – may be the fundamental drivers of a firm's high-growth rates.

Startups face critical challenges in prioritizing requirements under severe resource constraints. Although methods such as RICE and ICE are known, prior studies show that decisions often default to ad hoc judgments and stakeholder opinions. This paper introduces and validates VECTR, a lightweight prioritization framework that integrates three criteria highly relevant to startups: return on investment (ROI), Time-to-Value (TtV), and confidence. Rather than presenting these criteria as conceptually new, VECTR’s contribution lies in their explicit operationalization for startup contexts and their integration into a single lightweight visualization. Seventeen practitioners—including founders, product managers, and technical leads—participated in semi-structured interviews. They first described their current prioritization practices and then evaluated VECTR through explanatory material and a proof-of-concept visualization. Results show that ROI, TtV, and confidence are already considered in practice, but inconsistently. Practitioners found VECTR intuitive, visually clear, and useful for structuring discussions, while noting limitations related to estimation effort and subjective assumptions. Most reported that it could support better data-driven decisions, though concerns remained around input estimation and assumptions made by biased humans. This study contributes a financially grounded, software-startup-suitable prioritization method and offers practitioners a practical way to allocate scarce resources toward requirements with the greatest impact, while acknowledging that the validation demonstrates perceived usefulness rather than measurable performance gains.

Software startups operate in environments characterized by rapid change, high uncertainty, and limited resources, resulting in high failure rates and challenges such as premature scaling and cash flow mismanagement. Prior research on pre-MVP validation has largely measured activity by volume rather than by the strength of evidence produced, leaving open whether evidence type, rather than quantity, is associated with startup performance. This study addresses that gap by investigating how early-stage software startups validate their initial idea before building their first Minimum Viable Product (MVP). Through 29 semi-structured interviews with founders from 16 software startups, pre-MVP validation activities were extracted and inductively coded into a six-level Validation Canvas spanning three validation stages identified in the literature: problem validation, problem-solution fit, and product-market fit. Startup performance was assessed through a composite ranking across funding, revenue, profitability, and runway indicators, and validation activities were analyzed thematically to derive the six evidence levels. No clear relationship was observed between the number of validation events and startup performance. Instead, stronger-performing startups tended to reach higher levels of evidence—particularly securing contingent investment commitments (Level 5) or paying customers (Level 6) before full MVP development. Level 6—paying customers before the full product exists—is identified as the strongest form of pre-MVP market evidence, as it directly validates willingness-to-pay without relying on investor confidence. In this study, product-market fit is operationalised as demonstrated commercial viability through external financial commitments rather than interest signals or free sign-ups alone. Based on these exploratory findings, the study proposes the Hierarchy of Validation: a staged, bidirectional process model in which bottom-up traversal from informal interest signals (L1) toward paying customers (L6) emerged as the primary pattern among stronger-performing startups. A top-down direction, in which experienced founders begin at higher evidence levels and work downward, is proposed as a hypothesis for future research. To our knowledge, this is among the first accounts of pre-MVP validation that differentiates strength of evidence rather than volume of activity, contributing the Hierarchy of Validation as an original, exploratory framework for early-stage software startups. These findings remain exploratory and require validation in larger and more diverse samples.