Blooming in adversity: resilience as disciplined-opportunity sequencing in underdog founders

Theory and evidence often link entrepreneurial ventures to favorable preconditions: abundant resources (Davidsson and Honig, 2003; Unger et al., 2011), supportive family environments (Aldrich and Cliff, 2003), higher education (Huang et al., 2021), or fertile entrepreneurial ecosystems (Stam, 2015). Yet many founders begin from material or social disadvantage and still build ventures precisely where such favorable conditions are absent (Andric et al., 2024; Bhardwaj et al., 2023; Dutta and Forbes, 2025; Ge et al., 2022; Saiyed et al., 2023). This empirical regularity raises a fundamental question about process: how do founders convert persistent adversity into entrepreneurial momentum over time? Scarcity and exclusion can shape distinctive motivational and behavioral dynamics without precluding agency (Bort and Tötterman, 2023; Pidduck and Clark, 2021; Rindova et al., 2009), a pattern captured in research on challenge-based or adversity-driven entrepreneurship (Miller and Le Breton-Miller, 2017; Wiklund et al., 2018). Founders who begin ventures under structural, social, or cognitive constraints (whom we refer to as underdogs) provide a setting for examining the temporal mechanisms of entrepreneurial resilience.

Emerging work indicates that underdog entrepreneurs may reinterpret constraints as a source of motivation and direction for action (Bort and Tötterman, 2023; Saiyed et al., 2023), reframing constraint from a limitation into a condition that shapes entrepreneurial engagement. However, these studies leave open the temporal question of how such action is sustained and transformed over time. In this context, resilience has been conceptualized as a relatively stable individual disposition (Ahmed et al., 2022; Korber and McNaughton, 2018), an aggregate outcome (Branicki et al., 2018; Roundy et al., 2017), or an adaptive entrepreneurial capability (Donaldson et al., 2026; Manfield and Newey, 2018). Related work calls for research that connects “psychological traits, behavioral patterns, organizational characteristics, external environments, diverse disruptions, and the daily routines and practices that entrepreneurs enact when they prepare and respond to those challenges” (Korber and McNaughton, 2018, p. 1139). Miller and Le Breton-Miller (2017) likewise connect enduring hardship with entrepreneurial initiative. Building on this line of work, we focus on the micro-temporal conversion of repeated setbacks into sustained entrepreneurial action.

We distinguish between two temporally distinct forms of adversity: enduring baseline disadvantage and discrete setbacks arising during venture development. We define baseline disadvantage as enduring structural, social, or cognitive constraints present at or before venture entry, such as poverty, stigma, exclusion from influential networks, or other persistent forms of marginalization (Miller and Le Breton-Miller, 2017; Phillips and Ranganathan, 2025; Su et al., 2023). We treat this kind of disadvantage as a founder-level condition; it is distinct from, but often co-occurs with, a set of task-environment conditions under which the mechanism becomes especially visible, namely frequent and information-rich setbacks, tight resource margins, and thin buffers against error (Morris et al., 2022). Throughout, baseline disadvantage denotes founder-level conditions attached to the individual at venture entry, not environmental or industry features. By contrast, process setbacks are discrete adverse events that arise during venture development, such as lost clients, investor rejection, operational breakdown, or regulatory shocks (Shepherd and Williams, 2020; Williams et al., 2017). Against this background, we focus on how discrete venture-level setbacks are processed over time and how enduring disadvantage may shape the bandwidth, cadence, and consequences of these responses (Hartmann et al., 2022; Korber and McNaughton, 2018). Accordingly, we ask: How does entrepreneurial resilience operate as a temporal process under baseline disadvantage, and through which mechanisms do repeated setbacks become generative of entrepreneurial momentum?

We conduct a multiple-case comparative process study of 27 founders across four contrasting institutional settings (Bangladesh, Italy, Iran, and the United States) who faced documented baseline disadvantage across structural, social, and cognitive dimensions. Drawing on narrative interviews and secondary materials organized into case chronologies, we trace the micro-temporal mechanisms through which founders recognize, interpret, and act on adverse events over time.

Our study makes two main contributions. First, it contributes to resilience research in entrepreneurship (Hartmann et al., 2022; Korber and McNaughton, 2018; Williams et al., 2017), identifying a time-ordered conversion mechanism, which we term Disciplined-Opportunity Sequencing (DOS), that offers a processual account of how resilience can operate under constraint. The name reflects the core of the mechanism: founders turn setbacks into opportunity through a disciplined sequence of small probes, explicit readouts, and threshold updates. Within this account, we conceptualize resilient agency as action activated by adversity, enacted through iterative DOS cycles, and directed toward generating momentum, as reflected in the temporal patterning of the process and in its breakdowns (e.g. stall, flood, churn). Second, it contributes to adversity-driven and challenge-based entrepreneurship (Miller and Le Breton-Miller, 2017; Wiklund et al., 2018): we specify DOS as a process framework of entrepreneurial resilience in which resilient agency is the running cycle itself, and identify a temporal architecture through which disadvantage can become generative. Founder-level baseline disadvantage provides the study setting, while the cycle's proposed boundary conditions lie in the task environment; the tight resource margins and thin buffers observed in this setting make its temporal dynamics especially observable.

Much of entrepreneurship research explains venture emergence under favorable conditions, emphasizing valuable and rare resources (Barney, 1991), human capital such as education and experience (Davidsson and Honig, 2003; Unger et al., 2011), and supportive networks and institutional environments (Stam, 2015).

Studies of entrepreneurship under disadvantage examine how ventures are initiated and sustained in contexts of poverty, institutional voids, and marginalization. For example, scholars examine how resource scarcity shapes opportunity recognition and action (Miller and Le Breton-Miller, 2017; Sarkar and Mateus, 2024; Van Burg et al., 2012), how entrepreneurs respond to persistent adversity, resource constraints, and unstable institutional contexts (Busch and Barkema, 2021; De Massis et al., 2018; Shepherd and Williams, 2020; Welter et al., 2018), and how fragility and the “liability of poorness” constrain growth trajectories (Morris et al., 2022; Shantz et al., 2018). In parallel, research on entrepreneurial resilience has examined how entrepreneurs cope with stress and disruption. For instance, studies examine how grief recovery unfolds after failure (Shepherd, 2003), how entrepreneurs “bounce forward” through learning from setbacks (Korber and McNaughton, 2018), and how psychological resilience relates to venture survival and coping under pressure (Ahmed et al., 2022; Chadwick and Raver, 2020). We focus on how repeated setbacks are processed as inputs to action over time: how they are interpreted, acted upon, and recursively incorporated into subsequent decisions in ways that renew agency and accumulate into entrepreneurial momentum (Williams et al., 2017).

Here, we use these conversations as sensitizing lenses for specifying resilience as a temporal process (Bowen, 2006; Coffey and Atkinson, 1996). When founders enter venture creation under baseline disadvantage, constraints are often material, relational, and institutional at once (Maalaoui et al., 2020): capital is thin, incomes are unstable, market access is restricted, elite ties are weak or absent, and structural bias or regulatory neglect shapes what opportunities can be pursued and at what cost (Bort and Tötterman, 2023; Su et al., 2023; Morris et al., 2022; Webb et al., 2013). Under such conditions, adverse events may be less easily absorbed as temporary noise. Setbacks may be more frequent, more information-rich, and more consequential. Their interpretation and use become central to ongoing action. They can alter liquidity, legitimacy, and room for maneuver at the same time; disadvantage may therefore shape the scale of action, the speed of feedback, and the consequences of error (Desa and Basu, 2013; Morris et al., 2022; Shepherd and Williams, 2020; Williams et al., 2017). Enduring hardship may function as impetus for entrepreneurial engagement, beyond its constraining role (Miller and Le Breton-Miller, 2017; Shepherd and Williams, 2020). Under these founder-level conditions, adversity may sharpen attention to overlooked problems, reinforce persistence, and loosen attachment to dominant institutional logics, while also intensifying fear, vulnerability, and exposure, especially where failure and even success carry immediate social and material consequences (Miller and Le Breton-Miller, 2017; Morris et al., 2022; Shepherd and Williams, 2020). This shift does not happen by default, and it comes at a cost: when safety nets are thin, interpreting adversity as information rather than as confirmation of exclusion may shape whether action continues or collapses (Cacciotti et al., 2016; Jenkins et al., 2014; Powell and Baker, 2014; Singh et al., 2007).

Resilience has been defined as adaptive functioning through interaction with the environment before, during, and after adversity (Williams et al., 2017), a construct with roots in positive psychology (Luthans and Youssef, 2007) that entrepreneurship research has progressively adapted to venture contexts (Ayala and Manzano, 2014). Building on this view, resilience exceeds persistence by linking adversity to agency through appraisal, emotional regulation, and situated action, and it becomes visible less in isolated responses than in patterned re-engagement across episodes of setback (Cacciotti et al., 2016; Korber and McNaughton, 2018). These episodes do not stand alone; they form sequences in which each response reshapes the conditions for subsequent action (Williams et al., 2017). We therefore treat resilience not as a stable personal endowment or a retrospective outcome label, but as a temporally unfolding sequence through which adversity is appraised, affect is regulated, thresholds are reassessed, and action is renewed (Korber and McNaughton, 2018; Williams et al., 2017).

We define resilient agency as purposeful action activated by adversity, sustained through iterative cycles of interpretation, emotional regulation, feedback, and renewed engagement with the environment, and oriented toward forward momentum under constraint. Three properties distinguish it from agency in general. First, its activation is conditional on adversity rather than routine engagement. Second, its operation is sequential and recursive instead of episodic. Third, its trajectory is generative, producing new possibilities in the form of customers, markets, legitimacy, and learning.

When capital is scarce, legitimacy is fragile, and support structures are weak, setbacks are more frequent, more consequential, and less easily buffered (Miller and Le Breton-Miller, 2017; Morris et al., 2022; Su et al., 2023). Under such conditions, we look beyond persistence alone to examine the transition from disruption to renewed action. We therefore examine whether founders repeatedly recode adverse experience into information, regulate its emotional weight, and re-enter action in ways that preserve the capacity to continue. This form of agency is observed in sustained sequences of action, whereas its absence is revealed when those sequences break down.

Building on recent definitions of resilience as context-sensitive and evolving (Hartmann et al., 2022), we specify the micro-temporal sequences through which it becomes productive in entrepreneurial settings. These sequences describe how adverse events are interpreted, acted upon, and recursively incorporated into subsequent action under constraint. To unpack this temporal process, we draw on three interconnected streams of research. A first stream concerns emotional regulation. Entrepreneurs must manage fear, frustration, and uncertainty where feedback is ambiguous and external validation is scarce. Emotions such as fear can deter or drive action depending on how they are appraised and channelled (Morgan and Sisak, 2016; Welpe et al., 2012). Studies of entrepreneurial affect show that fear of failure, when constructively processed, can heighten effort and strategic caution (Cacciotti et al., 2016; Cacciotti and Hayton, 2015). Entrepreneurial emotions, such as desire for redemption or proof of worth, add motivational energy that keeps effort purposeful (Cacciotti et al., 2016; Cardon et al., 2012; Powell and Baker, 2014). Such regulation enables continued engagement through rejection and uncertainty, a form of affective persistence central to resilient agency (Shepherd, 2003; Stephan, 2018). Under baseline disadvantage, thinner safety nets and greater identity threat may make emotional regulation especially consequential for continued engagement.

A second stream concerns recursive feedback. Through repeated experimentation (small probes), feedback completes and renews the cycle. Early actions generate experiential and emotional cues that reinforce or redirect subsequent behavior (Fisher, 2012; Shepherd, 2003). Work on entrepreneurial failure and recovery shows how learning and renewed engagement produce “bounce forward” (Korber and McNaughton, 2018; Shepherd, 2003). These feedback loops gradually stabilize action and help entrepreneurs convert ambiguous experiences into workable heuristics for future action. Motivational orientation may shape how cycles unfold, for example through shifts between more cautious and more exploratory stances (Higgins, 1997). Scholars link these orientations to differing patterns of exploration and risk taking (Brockner et al., 2004; Hmieleski and Baron, 2008). Under initial disadvantage, such orientations shape how founders frame setbacks, what experiments they pursue, and how persistently they iterate through feedback.

A third stream concerns action under uncertainty. Research in this area highlights practices such as affordable-loss thinking and resource improvisation, which can support iterative action when predictive planning is difficult (Baker and Nelson, 2005; Dew, 2009; Sarasvathy, 2001). Such perspectives suggest a feedback-driven process through which founders read adversity, regulate its emotional weight, and act iteratively under uncertainty. While effectuation and bricolage offer foundational logics for acting under uncertainty, they give less explicit attention to how discrete setbacks are sequentially processed and converted into renewed action over time.

We employed a multiple case (Wadham and Warren, 2014) and qualitative process design to induce a time-ordered account of how founders facing baseline disadvantage convert process setbacks into entrepreneurial momentum. A multiple-case, qualitative process design was chosen because it is suited to tracing evolving mechanisms while preserving contextual richness (e.g. Aversa et al., 2021), including through narrative accounts (Kaandorp et al., 2020; Langley, 1999; Shankar et al., 2023), and because the research question requires temporal granularity for which cross-sectional or variance-based designs are less suited. Resilience-as-process and action-under-uncertainty served as sensitizing lenses for examining the micro-temporal ways in which founders sustained agency under constraint.

We began with a broad puzzle (why some founders under disadvantage succeed despite persistent adversity) and purposefully selected founders who met ex-ante baseline disadvantage criteria. As analysis progressed, a consistent pattern emerged: across independent accounts, setbacks contained informational cues that founders worked via bounded small, reversible probes. This observation directed the analysis toward a time-ordered conversion mechanism that we termed DOS. We adopted a theoretical sampling strategy (Strauss and Corbin, 1998), adding further cases to vary probe cost, feedback frequency, and institutional context, and to include negative/contrast cases that interrogated scope conditions. The constant comparative method (Strauss and Corbin, 1998) guided integration from raw data to higher-order constructs (Gehman et al., 2018).

Sample construction proceeded in three stages. In Stage 1 (purposeful selection), an initial set of founders was identified that, based on publicly available information and gatekeeper referrals, met two ex-ante founder-level criteria of baseline disadvantage: enduring constraint along at least one structural dimension (e.g. limited finance, precarious legal status, exclusionary networks) and at least one socio-cognitive dimension (e.g. stigma, educational gaps, social isolation). Selection was thus based on attributes of the individual founder; features of the industry or market environment in which the venture operated were not considered. Founder-level dimensions were assessed through a combination of founder-reported accounts and corroborating secondary evidence where possible (e.g. press coverage, financial disclosures, gatekeeper assessments, educational records where available) rather than relying on self-categorization alone; thresholds were context-relative (e.g. an educational gap was documented when the founder lacked formal qualifications typically expected in their industry or market setting) following the multi-dimensional operationalization of disadvantage in challenge-based entrepreneurship (Miller and Le Breton-Miller, 2017). In Stage 2 (screening and interviewing), we verified these conditions through the first interview session and secondary data, retaining only founders for whom baseline disadvantage could be documented. In Stage 3 (theoretical sampling), as analysis progressed and the conversion pattern became visible, we added further cases to vary probe cost, feedback frequency, and institutional context, and to include negative/contrast cases in which the conversion logic broke down.

We sampled theoretically, so cases entered as the mechanism took shape. Stage 2 kept only founders whose baseline disadvantage was corroborated by evidence; Stage 3 added cases theoretically and by snowball, leading to a final corpus that comprises 27 founders (Table 1), including 9 cases with contrastive cycle episodes in which DOS temporarily stalled, flooded, or churned. In this phase, these within-corpus episodes were triangulated with 7 secondary shadow-contrast cases from public sources (Table A1, online appendix). We define venture progress at t₀–t as sustained, externally verified market traction (e.g. revenue maintained across multiple consecutive quarters) together with material third-party validation (e.g. signed commercial contracts beyond pilots, regulatory clearance, or repeat enterprise customers), corroborated by public documents from secondary sources for shadow-contrast cases. For the most recently launched ventures in the corpus (≤2 years at data collection), we asked for documented evidence of active market engagement and initial traction (e.g. completed sales cycles, repeat orders, or operational milestones). The final corpus spans diverse industries, locations, and adversity profiles.

Initial participants were identified through entrepreneurship support organizations, business incubators, and entrepreneurship education programs that specifically work with underserved communities. Additional participants were recruited through snowball sampling, with initial participants recommending others who fit the study criteria. The final sample consisted of 27 entrepreneurs (5 female, 22 male). Entrepreneurs' ages ranged from 24 to 74 years (mean = 39.37; SD = 13.30), and their ventures had been operating between 1 and 47 years (mean = 12.00; SD = 11.33) at the time of data collection. All participants were assigned unique IDs; sensitive details were de-identified. We followed a replication logic (literal and theoretical) across cases. We also used negative and contrast evidence from within the primary corpus and from secondary shadow cases to examine and triangulate where cycles stalled, flooded, or churned; flood appeared within the primary corpus but not as a terminal failure pattern in the secondary cases.

Data were initially collected through in-depth narrative interviews conducted between January 2023 and March 2024. Follow-up and secondary data collection ended in November 2025. Each participant engaged in two interview sessions. The first interview (90–120 min) employed an open narrative approach, beginning with the prompt: “Please tell me the story of your entrepreneurial journey, starting from your earliest recollections of circumstances that may have shaped your path”. This unstructured beginning allowed participants to frame their narratives on their own terms, revealing how they positioned their entrepreneurial identities in relation to early experiences (Larty and Hamilton, 2011).

Following this open narrative, the interview shifted to a semi-structured format focusing on specific aspects of the entrepreneurial journey: initial conditions and constraints, triggering events, decision-making processes during key transitions, emotional experiences, unexpected outcomes, and reflections on personal transformation. The second interview (60–90 min), conducted 2–4 weeks after the first, allowed for follow-up questions, clarification of timeline details, and deeper exploration of themes that emerged during initial analysis.

Interviews were conducted in person where possible (21 participants) or via video conference (6 participants), audio-recorded with permission, and transcribed verbatim. Field notes documenting non-verbal cues, emotional responses, and immediate analytical insights complemented the interview transcripts (Strauss and Corbin, 1998). Additionally, supplementary data sources included business plans, media coverage, social media posts, and other documents provided by participants that chronicled their entrepreneurial journeys, allowing for contextual enrichment of narrative accounts. We also compiled a public secondary-source corpus including press coverage, public talks/podcasts, social media posts, accelerator/investor materials, registry filings, and publicly available company documents. Participant-provided documents and public secondary sources were linked to the case timelines to corroborate dates and decisions.

Contrastive evidence was incorporated at two levels. Within the primary corpus, cases marked with † in Table 1 contained cycle-level episodes of stall, flood, or churn; these were coded at the cycle level rather than treated as negative cases at the case level. Beyond the primary corpus, the 7 secondary shadow-contrast cases reported in Table A1 were assembled from public, time-stamped sources and selected on three criteria: (1) documented baseline disadvantage comparable to the focal cases; (2) externally verified absence of sustained venture progress or evidence of cycle-level breakdown; and (3) sufficient evidence depth, defined as 60–90 min of verifiable interviews or long-form material and ≥3 independent source types. These cases were selected for diversity in sector, institutional contexts, and breakdown dynamics. Flood did not emerge as a terminal failure pattern in these secondary cases, although it was observed at the cycle level within the primary corpus.

Our analysis followed an abductive, active-categorization approach (Grodal et al., 2021). Abduction involves iterating between empirical observations and candidate theoretical explanations: surprising patterns in the data prompt tentative conceptual framings, which are then tested against further evidence and refined (Strauss and Corbin, 1998). In our case, the sensitizing lenses introduced in Section 2 (resilience-as-process and action-under-uncertainty) provided the initial conceptual vocabulary, but the specific mechanism (DOS) and its components emerged from the data through this iterative cycle of pattern recognition and theoretical probing. We integrated narrative analysis (constructing case-level chronologies from data elements) with analysis of narratives (identifying cross-case themes) and combined our analytical coding with temporal bracketing and event sequencing to surface patterned, time-ordered relations (Gioia et al., 2013; Langley, 1999; Polkinghorne, 1995).

Phase 1: Familiarization and open coding. For each founder, after the interviews we constructed a case chronology by integrating interview accounts with time-stamped artifacts where possible (e.g. emails, filings, press coverage, other materials) and cross-checked dates and event ordering during transcript review. The process followed Polkinghorne's (1995) distinction between “analysis of narratives” (identifying themes across stories) and “narrative analysis” (constructing explanatory stories from data elements) to understand each entrepreneurial journey on its own terms. The chronologies mapped episodes in which founders confronted jolts, recorded contemporaneous decisions and emotions, and annotated prevailing prevention-promotion orientation. Early readings cast setbacks as obstacles; repeated returns to the material suggested a different possibility: founders were working setbacks as information. This conjecture guided initial coding while keeping us close to informant language. Through open coding (Strauss and Corbin, 1998), we labeled segments that captured how actors noticed mismatches, bounded small, reversible probes, read outcomes, and adjusted thresholds. Analytic memos tracked emerging ideas and alternative interpretations.

Phase 2: Active categorization and data structure. Our analysis combined open coding with the active categorization moves of merging, splitting, relating/contrasting, and sequencing categories to move from 147 informant-near codes to 11 second-order themes and 5 aggregate dimensions (Grodal et al., 2021) (Tables 2 and 3). Guided by periodic “step-backs” to relevant literatures (Grodal et al., 2021; Strauss and Corbin, 1998), we divided, deleted, merged, and altered first-order codes to form a manageable set of abstracted first-order categories, which cohered into 11 second-order themes. These elements formed a provisional data structure (Tables 2 and 3) and a preliminary conversion sequence in which founders first narrowed exposure under prevention and affordable loss, then selectively expanded bets under promotion contingent on readouts. The second-order themes, in turn, organized into five overarching dimensions central to the DOS mechanism: Antecedents/Triggering, Conversion Routines, Controls, Breakdown modes, and Outcomes. Following established practice in qualitative process research (e.g. Gioia et al., 2013; Gehman et al., 2018), we present the data structure in the Methodology section as the analytical scaffold; the labels reflect the final coding vocabulary, while how these elements operate and combine over time is developed in Section 4 (Findings). To aid reading of the data structure, we specify here what each coding label denotes. A probe is a small, reversible experiment bounded by what the founder can afford to lose. A readout is the founder's near-term reading of a probe on three currencies: money, legitimacy, and learning. A threshold update is the revised go/no-go bar the founder sets for the next cycle. Exposure bandwidth denotes how much a founder risks in a single cycle, and temporal cadence denotes how quickly one cycle follows another. We also label three ways the cycle can break down: stall, flood, and churn, which we define where they appear in Section 4.4.

Phase 3: Temporal sequencing and process framework. To examine the temporal ordering of the mechanism, we related and contrasted categories across cases and sequenced them on case-by-cycle timelines to identify processual linkages. Concretely, for each case we laid out the second-order themes on a timeline, marked the entry and exit points of each cycle, and compared the ordering across cases to identify recurrent sequences and deviations. By “cycle” we mean a bounded episode in which a founder moves from a setback (trigger) through interpretation, probing, and readout to a threshold update, a unit of analysis visible in the case chronologies and traceable through the time-stamped artifacts. At each turn, consistent with the extended case method (Wadham and Warren, 2014), we searched for disconfirming evidence and actively incorporated negative and contrast cases in which the putative sequence did not materialize or produced the breakdown modes identified above; this step sharpened scope conditions by specifying the contexts and configurations under which DOS is unlikely to operate.

We then translated the data structure into a process framework (Strauss and Corbin, 1998), using the “relating” move described in qualitative process research (e.g. Grodal et al., 2021). This translation (Figure 1) preserved temporal ordering while articulating linkages among routines, controls, and outcomes and situating DOS relative to adjacent literatures on adversity-driven entrepreneurship and entrepreneurial process.

We took several steps to strengthen interpretive rigor. Two researchers independently coded an initial subset of the corpus and reconciled discrepancies to stabilize the codebook; one researcher then completed coding while both continued to write memos and document decisions, producing an audit trail that includes codebook versions, meeting notes, and matrix snapshots. We mitigated hindsight and success biases by anchoring narratives to external timestamps and cross-checking interview accounts against time-stamped documentary evidence where possible (Strauss and Corbin, 1998). In keeping with an extended-case orientation, we treated rigor as dialogic: we iterated interpretations with practitioners who routinely support founders facing disadvantage and engaged participants across stages, and used their feedback to clarify anomalies and to locate micro-level accounts within broader structures. Member checks were conducted with a subset of participants (n = 7), which corroborated fit while surfacing nuances incorporated into the final process framework (Wadham and Warren, 2014).

Additional cases ceased to add new routines, controls, or breakdown modes pertinent to DOS and instead produced recurrent instantiations of existing categories. Within our corpus (cases in Table 1 marked with †), we coded cycle-level breakdowns as (1) Stall (over-prevention), (2) Flood (over-promotion), and (3) Churn (mistimed switching), each linked to control misfits in exposure bandwidth and/or temporal cadence. These contrastive instances were not excluded; instead, we analyzed them to specify scope conditions (when and why DOS fails) and to guard against survivorship bias. Beyond our focal 27 cases, we conducted a non-exhaustive environmental scan of public, time-stamped founder/venture narratives; Table A1 reports 7 illustrative examples selected for diversity in sectors, institutional contexts, and breakdown dynamics, used for triangulation.

Table A2 (online Appendix) reports a Case × DOS component matrix showing the presence of each routine, control, breakdown mode, and outcome register across all 27 cases. The matrix documents component presence, not intensity; variation in form and salience is traced in the Findings (Sections 4.1–4.5).

“I had no savings, no investors, and worked from a borrowed desk”. That is where I4 started, making eco-friendly packaging in Dhaka. He delivered the bags himself by bicycle. He gave free samples and said, “Just try it once, if you don't like it, don't take it”. When supplies ran out, he reused leftover materials. The venture grew into a small firm of 35 employees. Across the corpus, a recurrent pattern emerged. A setback lands. The founder reads it as information about what to try next. He runs something small and cheap he can walk back if it fails. He checks fast whether it worked, in money, in standing with other people, in what he learned. Then he decides how much to risk at once and how soon to try again. We call this recurring loop Disciplined-Opportunity Sequencing (DOS).

DOS runs as a cycle that begins when a founder reframes a setback as a signal (Setback-to-Signal), then moves through three routines: (R1) probes that are small reversible experiments bounded by what the founder can lose; (R2) rapid readouts of each probe on three currencies (money, legitimacy, and learning); and (R3) threshold updates that raise or lower the bar for the next cycle and time the switch from prevention to promotion (from caution to expansion). Setback-to-Signal reframing links the antecedent conditions to the DOS routines as the activation premise of the cycle (Figure 1). Two controls govern the cycle: exposure bandwidth (how much a founder risks at once) and temporal cadence (how fast one cycle follows the next). When the cycle runs well, it can build toward entrepreneurial self-realization (financial stability, personal growth, and social recognition). When the controls are misaligned, it breaks down into stall (over-prevention), flood (over-promotion), or churn (mistimed switching); these breakdown modes mark the limits of the mechanism (Tables 2 and 3; Table A1).

Founders in our sample routinely reframe setbacks as diagnostic signals in place of terminal failures. That reframing opens the DOS cycle that follows (Figure 1; Table 2). As one put it, “People doubted me because of my background” (I19). Others faced institutional opacity and stigma: “At that time, there were maybe 4–5 men in the whole country doing beauty work for women … the police raided the salon and fined us … if a photo came out showing me applying makeup on a woman, the first time I'd get a warning, the second time I'd be banned” (I13); “No matter who you talk to … bribery … under-the-table deals … the elevator industry … is deeply corrupt” (I26); “There's favoritism … if I post, my page gets taken down immediately” (I13); “At first … people thought I was too young … I lost some clients” (I25). Material conditions were often severe: “I had no savings, no investors, and worked from a borrowed desk” (I4); “Sometimes I struggled to pay rent” (I7); “I started with only 5,000 BDT” (I10). Relationally, support was scarce: “Actually I didn't get any support from anyone. No one told me to do business” (I1); “My family did not support me … I took the step by myself” (I2); “I had to teach myself everything from scratch, no teacher, no formal learning, just trial and error” (I24); “I wish I had a mentor … you figure it out all on your own” (I18).

Across cases, founders first recast misfortune as information. Lost customers, investor rejections, or operational glitches were described as cues about unmet needs, weak assumptions, or channel misfit. Yet founders showed dispositional activation and a dual engine of approach and avoidance. “If I waited for others to believe in me, I'd still be waiting” (I2); “I had doubts … but one thing I had was self-belief” (I3); “Believe in yourself. Even if no one else does, you should” (I6); “I'll build my own production unit and define the standards myself” (I22); “Screw what people think, I'll do what I need to” (I23). Motivation drew both from aspiration and fear: “[At the beginning] I was afraid, yes, but poverty was scarier. That's why I say, poverty makes you brave” (I6); “Sometimes I think, what if it all collapses again? But then I remind myself how far I've come” (I23). These conditions set the stage for the premise we term Setback-to-Signal reframing: “Every attempt to block me became an opportunity. That was my only survival strategy” (I26).

Our evidence suggests that these antecedent conditions shape how the DOS cycle operates. Where stigma is greater, each legitimacy readout carries disproportionate weight. Thin safety nets raise the cost of every breakdown mode. The Setback-to-Signal premise appears to mark the activation of resilient agency: the point at which adversity ceases to be a static condition and becomes a processual trigger. Cycle episodes in which founders entered or re-entered DOS differed from stall episodes (e.g. I5) in this interpretive shift: reading setbacks as information, a move away from confirmation of limitation.

The three routines are where resilient agency becomes concrete, since each turn of the cycle is agency enacted under constraint. Given the Setback-to-Signal premise, founders engaged a DOS cycle of three routines (Figure 1; Table 2): run affordable-loss probes (R1), take rapid readouts on money, legitimacy and learning (R2), and then update thresholds to time the prevention to promotion switch (R3).

• Routine 1: Affordable-loss probes. Founders ran tiny, reversible tests bounded by what they could afford to lose, often improvised and deliberately short to force feedback.

Illustrative evidence: R1 Affordable-loss probes. “With very limited capital, I had to start very modestly. I bought an old machine that often broke down. I used to buy milk and sugar on credit.” (I8); “I started with only 5,000 BDT, which barely covered raw materials.” (I10); “When supplies ran out, I reused leftover materials.” (I4); “It's better to act with uncertainty than to stand still.” (I3); “When things went wrong, I quickly adjusted and tried again.” (I6); “I used to deliver the bags myself by bicycle … I used to give free samples and say, ‘Just try it once, if you don't like it, don't take it.’” (I4)

• Routine 2: Rapid readouts (money, legitimacy, learning). Probes were evaluated against explicit, near-term indicators: money (repeat orders, revenues), legitimacy (visibility/endorsement), learning (contract/process updates). Weak bets were terminated; strong bets were scaled.

Illustrative evidence: R2 Readouts (money, legitimacy, learning). Money: “My first order came in […] It felt like I'd earned a million! […] when we completed our first 1,000 orders, that day felt like a dream.” (I3). Money: “In the 2024–25 financial year, we've seen 15% more profit than last year.” (I3). Money: “We … average monthly sales of around 400,000–500,000 Taka.” (I6). Legitimacy: “Within my first 10–12 posts, I reached 200,000 followers. I hadn't expected [that kind of] visibility.” (I13). Learning: “Every new incident became a new clause in our customer contracts. Nothing was instant; it all came through learning.” (I23). Learning/Money discipline: “If we add full payment method … we get most of them real customers. But with cash on delivery, we received fake orders most of the time.” (I2).

• Routine 3: Threshold updates and timed prevention to promotion switch. Following readouts, founders reset the go/no-go bar and timed a switch in stance (from caution to expansion): widen exposure (promotion) only when evidence and slack accumulate; tighten exposure (prevention) and shorten cycle time when signals soften.

Illustrative evidence: R3 Threshold updates and timed switch. “Every quarter, it's an inflection point. And that is not boring. So, there's constant evolution in my role.” (I18). “Now we know where to make it. Now it's easier than before. Hopefully, it will increase day by day.” (I1). “I've always said, this won't last, whether good or bad. You just keep moving.” (I23)

These probes were small enough to be repeatable yet concrete enough to generate decisive feedback. Across cases, probes clustered in two types: exploitative (remove an immediate constraint) and exploratory (test an adjacent possibility). Founders routinely ran one probe at a time or two to three in parallel, bounded by their exposure bandwidth, and decided in advance what they were willing to lose (money, time, goodwill) in each cycle.

Probes were evaluated rapidly on money (orders/revenue), legitimacy (credible endorsements, partner interest), and learning (validated or falsified assumptions). Founders treated each readout as a measurement rather than a mood: “If customers return and send friends, that's the win; money comes later, respect first” (I6), one said, foregrounding legitimacy as both a metric and fuel for subsequent cycles.

To discipline interpretation, teams time-stamped probe windows and pinned outcomes to case timelines (e.g. “cash booked,” “press hit landed,” “hypothesis retired”). These readouts fed directly back into both beliefs and emotions, updating the founder's read of the situation. Following readout, founders reset go/no-go thresholds (what “good enough” looks like next cycle) and timed the shift from a cautious (prevention) to an expansive (promotion) stance. When evidence accumulated, they widened exposure (promotion) and diversified probes; when evidence was weak/ambiguous, they tightened exposure (prevention) and shortened cycle time to reduce waste. Several respondents explicitly described pauses to “look from the hill”, then re-entry with a broader or narrower set of bets.

The three routines operated with varying intensity across cases, shaped by baseline disadvantage. Founders with the most constrained financial position (e.g. I10 starting with 5,000 BDT; I4 working from a borrowed desk; I8 buying on credit; I27 launching micro-batches with no formal training and limited inventory capital) ran the tightest probes, often a single probe at a time. Where legitimacy was the binding currency, as for I13, operating under regulatory stigma where each visibility gain had to be weighed against enforcement risk, readouts took longer and the switch to promotion was more cautious. The threshold updates in Routine 3 were correspondingly conservative: under narrow bandwidth, founders required stronger evidence before widening exposure because the cost of a wrong bet was proportionally higher. I3's rapid scaling (from first order to 1,000 orders) illustrates what happens when early money readouts are strong and bandwidth loosens; I5's stall episodes illustrate the opposite. These contrasts show that DOS did not run as a uniform sequence; across founders, its tempo and texture varied with how tightly constrained they were. Across repeated cycles, some founders also became quicker to read unfamiliar setbacks, sorting and acting on them as familiar kinds of signals.

Two controls shape how each DOS cycle unfolds: how much a founder risks in a single cycle (exposure bandwidth) and how quickly one cycle follows another (temporal cadence). Early on, founders kept each bet small and reversible, and widened their exposure only once small wins created room to do so. They also adjusted their pace, moving faster and in smaller steps after a setback and slowing down as evidence accumulated, while unstable economic conditions sometimes forced them to plan only a few weeks ahead (Table 3). Across cases, variation in these two controls corresponded with differences between sustained cycling and breakdown episodes.

Illustrative evidence: Controls. Exposure kept small at the start: “I saved money from tutoring to buy a second-hand laptop. That's when I started doing things little by little.” (I3). Very limited resources: “Sometimes I struggled to pay rent.” (I7). Small, reversible bets: “I used to give free samples—‘try it once, if you don't like it, don't take it.’” (I4). Faster cycles after a setback: “When things went wrong, I quickly adjusted and tried again.” (I6). Regular review as evidence built: “Every quarter, it's an inflection point.” (I18). Short planning horizon under economic instability: “I can only predict 10–20% of what's going to happen [… ] inflation [ … ] makes it impossible to stick to the plans.” (I22). Founders entered the cycle with narrow slack, so for many founders, exposure bandwidth was not a free choice; narrow slack imposed it, and it loosened only as successful cycles generated surplus. Although our construct is founder-level, how founders set the two controls was also shaped by task-environment conditions: regulatory-enforcement rhythms for I13 and I26, seasonal demand for production-based ventures like I6 and I8, and macroeconomic volatility for I22. We treat these as environment-level boundary influences on how the controls were set. Within our disadvantaged cases, the calibration margin was often thin, and misalignment could trigger breakdown modes rapidly; conversely, when calibration improved across cycles, founders became better able to recognize which events merited action and which did not. Whether entrepreneurs under advantaged conditions set these controls more freely remains to be tested comparatively. This makes baseline disadvantage a revealing setting for studying resilience-as-process, because cycle calibration and breakdown are especially observable under tight margins.

We coded cycle-level breakdown within the corpus († in Tables 1 and 3) and triangulated with public shadow contrasts (Table A1). Stall (over-prevention) appears as hesitation and probe avoidance aftershocks or under tight liquidity. After a shock, some founders kept narrowing exposure, delaying probes; information acquisition collapsed and ventures decayed. Audit trails show elongated cycle times without corresponding evidence gains. Flood (over-promotion) arises when bandwidth widens without evidence, producing waste and losses. After an early win, others launched too many bets without disciplined readouts; losses accumulated and signal quality deteriorated. Churn (mistimed switching) reflects premature switching before signals mature and the coordination/legitimacy costs of oscillation. This produced confusion in partners and customers, eroding legitimacy.

Stall (over-prevention): exposure kept too narrow. After an early shock, the founder hesitated to re-enter the cycle: “Sometimes I felt I might have taken the wrong path. At one point, I wanted to give up. But I never lost faith” (I5). Stall manifested as probe avoidance and continued narrowing of exposure (see Table 3: Stall/over-prevention). With bandwidth constrained to near-zero, no readouts (money, legitimacy, learning) arrived to raise thresholds; cadence slowed and momentum stalled.

Flood (over-promotion): exposure widened without evidence. Operational over-extension produced losses: “The first six months were extremely difficult. Only 3–4 customers came a day … Food would often go to waste, and I faced losses” (I7). Aggressive push without disciplined readouts led to waste and negative money signals (Table 3: Flood). Exposure bandwidth exceeded the venture's ability to absorb downside; cadence did not slow accordingly, compounding losses.

Churn (mistimed switching): switching before the evidence matured. Cross-case synthesis (non-verbatim; see Table 3): several founders switched offerings before signals matured, confusing customers and eroding legitimacy; frequent reversals raised coordination costs (Table 3: Churn). In one case, contract terms were being updated “after every new experience” (I23), but cadence compressed so much that readouts were not allowed to stabilize, leading to oscillation rather than momentum.

The breakdown modes mark the conditions under which resilient agency does not materialize or ceases to operate. Stall corresponds to an affective block: the Setback-to-Signal reframing is delayed or weakened, and the founder reads adversity as confirmation rather than information. I5's account (“Sometimes I felt I might have taken the wrong path. At one point, I wanted to give up”) captures this hesitation before the cycle eventually resumed. Flood corresponds to a breakdown of the controls: agency is present but undisciplined, with probing outpacing readout capacity. Churn is a failure of timing: the cycle keeps running, but each turn closes before the signal settles. Together, these off-ramps specify when and why the DOS mechanism (and the resilient agency it enacts) breaks down.

Sustained DOS was associated with entrepreneurial self-realization across instrumental, psychological, and social registers, while setbacks also became increasingly readable as usable signals. Comparative evidence across our corpus and shadow contrasts suggests candidate scope conditions: DOS appears especially visible where uncertainty is high, setbacks are frequent and information-rich, probe costs are low, and safety nets are thin; whether more routinized, capital-abundant contexts favor goal-directed optimization remains a question for comparative research (Figure 1; Table A1).

Illustrative evidence: Outcomes. Instrumental: “Finally, I could pay debts and stabilize the company” (I18). Instrumental: “Income growth allowed me to expand and employ others.” (I18). Instrumental (opportunity capture): “A failed order led to a new overseas client.” (I5). Psychological: “I see myself as someone who builds rather than waits.” (I1)

Psychological: “Being an entrepreneur isn't only selling products, [it's about] being mentally independent, gaining confidence, and helping others.” (I3). Psychological: “Maybe if I'd skipped that, I'd be richer, but creating gives me joy.” (I13). Social: “Being invited to mentor others made me feel included.” (I17). Social: “Those who once feared it, now feel proud … I now see trust in my father's eyes” (I3). Social (customer meaning): “One office-goer said, ‘If I don't eat at your place, I feel full but not satisfied.’ These moments are my greatest achievements.” (I7).

Instrumental stability, psychological transformation, and social recognition appear to accumulate across sustained DOS cycling. The findings also suggest that, across advanced cycles, founders became quicker to turn new setbacks into workable inputs and to treat adversity as an expected feature of the venture. In the included cases, the evidence suggests a recursive relationship between founders' interpretive and emotional capacities and repeated DOS cycling over time.

This study examined how entrepreneurial resilience may operate as a temporal process under baseline disadvantage (Korber and McNaughton, 2018; Williams et al., 2017), tracing how founders can convert setbacks into entrepreneurial momentum under persistent constraint. The findings reveal DOS, a recurrent, time-ordered mechanism through which resilient agency is activated via Setback-to-Signal reframing, sustained through three conversion routines (probes, readouts, threshold updates), governed by two controls (exposure bandwidth, temporal cadence), and bounded by three breakdown modes (stall, flood, churn). Across our disadvantaged cases, the cycle often ran under narrow bandwidth and high legitimacy stakes.

Our paper therefore conceptualizes resilience as operating through a temporal conversion mechanism (DOS) enacted in situated routines under constraint (Hartmann et al., 2022; Williams et al., 2017). The mechanism's value lies in specifying the temporal architecture of conversion, including where it breaks. Several focal founders experienced cycle-level breakdowns alongside completed cycles, and the shadow contrasts document cases where the architecture collapsed entirely; this variation in cycle completion distinguishes DOS from a post-hoc account of resilient outcomes.

First, the study contributes to resilience research in entrepreneurship and positive psychology (Ayala and Manzano, 2014; Hartmann et al., 2022; Korber and McNaughton, 2018; Luthans and Youssef, 2007; Shepherd, 2003; Williams et al., 2017). Where recent reviews call for processual accounts that move beyond the trait–outcome ambiguity (Korber and McNaughton, 2018), we respond with DOS, a processual account of how capability endowments may be repeatedly assembled and redeployed in response to concrete adverse events (Williams et al., 2017; Hartmann et al., 2022). Consistent with Hartmann et al. (2022), the research design explicitly specifies and time-stamps adverse events and chronic constraints across cases (lost orders, investor withdrawals, regulatory shocks, stigma), keeping adversity central and observable rather than abstracting it into a background variable. This complements quantitative work linking entrepreneurs' psychological resilience to business survival via positive challenge appraisals (Chadwick and Raver, 2020) by tracing how such appraisals translate into sustained, iterative action. In line with Korber and McNaughton's (2018) call to define resilience as a process linked to a positive long-term trajectory, we show how the DOS cycles of probing, readout, and recalibration can underpin a cumulative “bounce forward” trajectory that culminates in self-realization in instrumental, psychological, and social terms.

Within this account, the study advances the conceptualization of resilient agency by giving it an observable form. The running cycle is resilient agency made observable (Williams et al., 2017), visible in the temporal patterning of entrepreneurial action, and its activation depends on the interpretive and emotional capacities founders bring to the reframing moment. Its absence is equally informative, since stall, flood, and churn identify the conditions under which resilient agency does not hold. This architecture also clarifies how DOS differs from adjacent action logics. Effectuation and DOS share an emphasis on affordable loss and action under uncertainty; DOS specifically centers cycles initiated when a discrete setback is reframed as a signal (Sarasvathy, 2001). Bricolage and DOS both emphasize action with resources at hand; DOS specifically centers the readout-and-threshold discipline through which actors decide when to stop, when to expand, and how fast to try again (Baker and Nelson, 2005). Lean experimentation and DOS both emphasize low-cost tests and fast feedback (Shepherd and Gruber, 2021); their primary objects differ, with lean experimentation centered on venture/offer refinement under market uncertainty and DOS on the temporal processing of adversity through exposure bandwidth and cadence. Pivot research theorizes the irreversible decision to redirect a venture (Flechas Chaparro and de Vasconcelos Gomes, 2021); DOS specifies the reversible probing that can precede and time it. The strongest distinction concerns breakdown: adjacent logics give less explicit attention to how disciplined iteration comes apart, while DOS identifies stall, flood, and churn as observable exits from resilient agency, each tied to misfit in the cycle's controls. By providing a criterion anchored in observable cycle episodes (their presence, frequency, completion, and breakdown in process data), with DOS we aim to respond to the broader mismatch between resilience conceptualization and operationalization highlighted in resilience research (Halekotte et al., 2025).

Second, the findings contribute to adversity-driven and challenge-based entrepreneurship (Miller and Le Breton-Miller, 2017; Wiklund et al., 2018) by tracing how disadvantage can become generative. Existing work establishes that adversity can serve as an “impetus for enterprise” (Miller and Le Breton-Miller, 2017), but leaves the temporal architecture of this conversion less fully specified. We contribute to this conversation by identifying the sequences of reframing, bounded experimentation, and regulated switching through which founders may work constraint into momentum. DOS accounts for variation in how founders respond to disadvantage, including why some cycles accumulate momentum whereas others stall (Langley, 1999). We treat founder-level baseline disadvantage as the study setting, and we locate the cycle's boundary conditions in the task environment. We propose that in this setting disadvantage tightens the exposure a founder can afford, raises the cost of each legitimacy readout, and makes the breakdown modes more damaging, pressures that make the cycle run faster and with less room for error. Negative and contrast cases, from primary and secondary data sources, showed us where cycles stalled, flooded, or churned. Flood, though, never became terminal in the shadow cases. We saw it only within the corpus. However, since every founder we considered faced disadvantage, comparison with less constrained founders could further test the proposition. This positioning retains analytical leverage over adversity without implying that reframing or resilience is exclusive to disadvantaged founders (Korber and McNaughton, 2018). Relatedly, some accounts hint that identity construction may intersect with the DOS cycle: experiences of exclusion or outsider status were sometimes reframed as distinctive assets (Lounsbury and Glynn, 2001; Zahra and Wright, 2011), a thread that warrants further investigation.

Analyzing the DOS mechanism under baseline disadvantage leads to actionable implications for ecosystem stakeholders. The mechanism's architecture (routines, controls, breakdown modes) provides a diagnostic framework that suggests points for intervention design.

For incubators and accelerators, the findings suggest moving beyond generic resilience training toward structured support targeting each routine. For R1, programs can help founders design affordable-loss probes with pre-defined stopping rules, as exemplified by founders such as I4, who started with free samples and a bicycle as a deliberate low-cost probe. For R2, mentors can train founders to read legitimacy and learning currencies alongside money signals: I13's rapid social-media reach, for instance, was a legitimacy readout that preceded any money return and shaped the subsequent promotion switch. For R3, peer-learning formats can help founders calibrate the prevention-to-promotion switch by exposing them to how others in comparable constraints have timed their pivots, a calibration that proved critical for I18, whose quarterly “inflection points” disciplined an otherwise volatile environment, a discipline consolidated after earlier churn episodes.

For investors, the controls offer diagnostic criteria. Exposure bandwidth and temporal cadence may provide diagnostic signals: a founder whose bandwidth is narrowing cycle after cycle may be approaching stall; one whose cadence is accelerating without corresponding readout improvement may be heading toward churn.

For policymakers, the breakdown modes suggest targeted interventions. Micro-grant programs with short disbursement cycles can maintain probe momentum when founders' own bandwidth contracts, reducing the risk of stall. Milestone-based funding with readout checkpoints can discipline expansion and prevent flood. Stable regulatory frameworks and predictable review cadences, the absence of which severely constrained founders like I13 and I26, reduce the external turbulence that forces premature switching and churn. More broadly, the findings call for a reassessment of selection criteria: conventional indicators of “readiness” may systematically underrepresent founders whose main asset is the activation capacity documented here. Broadening assessment frameworks to recognize disciplined persistence, problem reframing, and iterative learning could expand entrepreneurial access for founders who demonstrate the capability to convert adversity into momentum despite modest starting conditions.

Cross-case comparisons suggest candidate scope conditions for DOS: high uncertainty, frequent and information-rich setbacks, low probe costs, and thin buffers. These conditions characterize the founder-level baseline disadvantage setting studied here but are not exclusive to it; they may also characterize highly uncertain or post-disruption environments. Whether routinized, capital-abundant environments with stable search spaces favor goal-directed search and optimization over probe-and-switch logic remains to be tested comparatively. These observations begin to specify boundary conditions; cross-country variation (institutional trust, support resources) plausibly moderates these links (Hartmann et al., 2022). Consistent with an extended-case orientation (Wadham and Warren, 2014), our claims travel by analytical generalization rather than statistical inference; transferability is bounded by the scope conditions we specify: uncertainty, feedback frequency, probe cost, and institutional context (including the ability to bound downside via safety nets). Our design has limitations. First, retrospective accounts may involve recall and sensemaking biases; we mitigated this risk by anchoring narratives to time-stamped artifacts (e.g. emails, invoices, social posts) and aligning them to case timelines. Two features of the analysis counter post-hoc rationalization specifically: chronologies constructed after the interviews were anchored to time-stamped documentary evidence, allowing temporal ordering in retrospective accounts to be cross-checked; and the breakdown modes (stall, flood, churn) emerged as within-case deviations, where the same founders who completed DOS cycles also experienced breakdowns, which reduces the likelihood that the pattern is solely a narrative artifact. Second, translation drift may occur; we employed back-translation and bilingual spot checks. Third, the corpus contains many ventures with discernible progress; we addressed survivorship concerns by including negative/contrast cases and by explicitly coding breakdown modes. These contrastive breakdowns were central to theory building: they constrained scope, disciplined inference, and revealed control misfits in exposure bandwidth and/or temporal cadence that derail DOS.

Sustaining repeated cycles of probing, exposure to rejection, and recalibration without a safety net places significant demands on founders' mental health and well-being (Stephan, 2018; Wiklund et al., 2018), raising evidence-based questions for further research. Although we document instances of entrepreneurial self-realization (greater confidence, meaning, and social recognition), our evidence also points to episodes of exhaustion, anxiety, and doubt. The tension speaks directly to emerging concerns about the potential dark side of entrepreneurs' psychological resilience: the same routines that generate momentum can also create cumulative strain when probe cadence and exposure bandwidth exceed founders' emotional and material buffers (Hartmann et al., 2022; Korber and McNaughton, 2018). Supporting founders facing disadvantage thus requires more than injecting resources or offering generic training; it calls for designing interventions that scaffold emotional regulation, normalize oscillations between prevention and promotion, and help founders calibrate bandwidth and cadence in ways that are sustainable for both the venture and the individual.

A further limitation concerns the scope of our comparative claims. Because all our founders faced baseline disadvantage, we cannot separate what disadvantage does to the cycle from how the cycle runs more generally. That disadvantage tightens the cycle's tolerances or raises the stakes of its breakdown modes is therefore proposed rather than established, and testing it would require founders who run the same cycle without comparable constraints. We accordingly frame DOS as a general temporal framework of resilience whose particular expression under baseline disadvantage remains an open question for comparative work.

The four-country design captures institutional variation along key dimensions but does not exhaust all possible configurations. Future work can test the DOS mechanism and its controls with event-history or panel designs, instrument affordable-loss probes in field experiments, and pursue cross-sector and cross-national replications, including resource-rich contrast populations, to refine external validity, sharpen the breakdown modes (stall, flood, churn), and specify the conditions under which the mechanism operates beyond baseline disadvantage.

Founders who start from disadvantage meet setbacks that are frequent and costly. Some convert those setbacks into momentum. This study asked how, and traced resilience as it unfolds in time. Our answer is Disciplined-Opportunity Sequencing: a cycle in which founders reframe a setback as a signal, run small probes bounded by what they can lose, take rapid readouts on money, legitimacy, and learning, and update the bar for the next round.

Read this way, resilience has an observable form. Resilient agency is the loop in operation, present while the cycle runs, weakened or absent where it breaks into stall, flood, or churn. This gives research a unit it can count and compare: cycle episodes, their completion, their pace. Baseline disadvantage provides a setting in which the cycle is especially visible; in our cases, tight margins and thin buffers make cycle calibration and breakdown especially observable. It is also the study's limit. Because every founder we studied faced disadvantage, what disadvantage does to the cycle is stated here as a proposition, and comparison with less constrained founders can test it. For those who support founders, the architecture is actionable: probes can be designed, readouts trained, pivots calibrated. Success has many causes; we account for one. It is part of how, in our cases, founders bloomed in adversity.

The two authors contributed equally to the conceptualization, methodology, investigation, formal analysis, writing – original draft, and writing – review and editing of this work.

We thank the guest editors of the Special Issue, Unai Arzubiaga, Vanessa Diaz Moriana, Andreas Kallmuenzer, and Jonathan Bauweraerts, for their guidance and encouragement during the review process, and the anonymous reviewers for their constructive and insightful comments. We also gratefully acknowledge the students who participated in the workshop Qualitative Research in Social Sciences: Local Entrepreneurs and Sustainable Development Goals, held at the Department of Economics, Management, Mathematics and Statistics, University of Trieste, Trieste, Italy, from March to May 2025, including Nazaninzahra Kaffash, Nipa Patwary, and Timoteo Aurelio Thomas Rizzetto, for the valuable discussions, support, and engagement with the research project. We used generative AI tools to assist with grammar and readability; we reviewed and verified all outputs. All ideas, arguments, and interpretations are our own.

The supplementary material for this article can be found online.