Predicting viability of small businesses on the edge of failure

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Journal of Small Business Management
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Predicting viability of small businesses on the edge
of failure
Han Dieperink, Jan Adriaanse & Mark Dechesne
To cite this article: Han Dieperink, Jan Adriaanse & Mark Dechesne (16 Dec 2024): Predicting
viability of small businesses on the edge of failure, Journal of Small Business Management,
DOI: 10.1080/00472778.2024.2435506
To link to this article: https://doi.org/10.1080/00472778.2024.2435506
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Predicting viability of small businesses on the edge of
failure
Han Dieperink
a
, Jan Adriaanse
a
, and Mark Dechesne
b
a
Institute of Tax Law and Economics, Leiden University, The Netherlands;
b
Faculty of Governance and
Global Affairs (FGGA), Leiden University, The Netherlands
ABSTRACT
This study challenges traditional small business failure predic-
tion models, which, while eective in categorizing bankruptcy
in stable conditions, falter in predicting outcomes once rms are
in a state of nancial distress. Building on insights from small
and medium-sized enterprises (SME) failure prediction, business
turnaround, and entrepreneurial resilience, our analysis under-
scores the pivotal yet often neglected role of entrepreneurial
traits in business failure and survival. By analyzing 520 viability
audits of distressed businesses, we demonstrate that incorpor-
ating entrepreneurial characteristics renes the predictive accu-
racy of these models. Our ndings challenge the static nature of
traditional approaches, advocating for models that incorporate
dynamic, non-nancial variables to better capture the complex-
ities of distressed businesses. This research calls for a shift in
failure prediction methodologies to fully recognize the inu-
ence of entrepreneurial traits, capabilities, and behavior, oer-
ing a nuanced understanding crucial for stakeholders in the
small business ecosystem.
KEYWORDS
Small business failure; small
business viability;
entrepreneurial resilience
Introduction
The ability to accurately predict the viability of small businesses has become
increasingly critical within finance and management disciplines, especially
following the global financial crises of 2008–2009 and the COVID-19 pan-
demic. These crises have underscored the urgent need for rapid and precise
viability assessments to effectively support small businesses during financial
distress. Notably, during the onset of COVID-19 measures in 2020, the Dutch
government, supporting 35 percent of its entrepreneurs, found itself without
adequate tools to apply a viability criterion in financial aid allocation (Davies
et al., 2023).
Traditional models, like the Altman Z-Score developed in 1968, primarily
use financial ratios to forecast a firm’s future performance based on past data
(Altman, 1968). However, the limitations of relying solely on financial
CONTACT Han Dieperink j.p.dieperink@law.leidenuniv.nl Institute of Tax Law and Economics, Leiden
University, The Netherlands
JOURNAL OF SMALL BUSINESS MANAGEMENT
https://doi.org/10.1080/00472778.2024.2435506
© 2024 The Author(s). Published with license by Taylor & Francis Group, LLC.
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/
licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly
cited. The terms on which this article has been published allow the posting of the Accepted Manuscript in a repository by the author(s)
or with their consent.

indicators for predicting outcomes for small firms are well-documented,
underscoring the necessity to incorporate non-financial variables (Balcaen &
Ooghe, 2006; Ciampi et al., 2021; Lussier, 1995). Research also highlights the
relevance of integrating the entrepreneurial dimension to capture the complex
interplay of personal and business interests (Caliendo et al., 2020; Ciampi &
Gordini, 2013; Sarasvathy, 2001; Spence, 1999). A critical flaw in existing
prediction models is their static nature, which fails to account for the dynamic
processes underlying business operations and growth, thus necessitating
a shift toward more dynamic, contextually aware predictive models (Abebe
et al., 2011; Adriaanse et al., 2016; Blatz et al., 2006; Laitinen, 2008; Lukason &
Laitinen, 2019; Trahms et al., 2013).
Our research enhances understanding in this area by integrating insights
from three distinct but interrelated research domains: SME failure prediction,
business turnaround, and entrepreneurial resilience. This multidisciplinary
approach provides a comprehensive perspective on small business viability,
enriching conventional analysis frameworks and broadening the understand-
ing of what influences small business outcomes. We emphasize the critical role
of entrepreneurial resilience, traits like persistence, adaptability, and social
skills, as pivotal in determining the survival of small businesses. This focus
addresses a gap in the literature that has often overlooked the substantial
influence entrepreneurs have on their business’s continuity.
Moreover, our research introduces temporal dynamics into the failure
prediction discussion, acknowledging that business failure is a process influ-
enced by time-sensitive factors. This approach challenges the static nature of
traditional models and provides a more realistic framework for understanding
business outcomes. Specifically, we investigate the viability of small businesses
within contexts of financial distress, a timely issue given recent economic
disruptions. By incorporating the personal and behavioral attributes of entre-
preneurs into our predictive model, we offer new insights into how these traits
contribute to business resilience.
Drawing from a dataset of 520 viability audits of financially distressed small
businesses, we investigate whether these factors may act as significant pre-
dictors for business survival and potentially improve the predictive accuracy of
existing models. The viability audits, spanning 2011–2019, provided a rich
foundation for analyzing the interplay between business viability, managerial
decisions, and entrepreneurial characteristics in the context of financial dis-
tress. Initial findings reveal a striking resilience among small businesses, with
a high proportion surviving beyond the expectations of the auditors. This
resilience underscores the limitations of current prediction practices, which
may inaccurately deem viable businesses as destined for closure or, conversely,
sustain non-viable “zombie” businesses (Blažková & Dvouletý, 2022).
Through these contributions and novel approaches, our research seeks to
refine viability assessment models, providing more nuanced tools for
2H. DIEPERINK ET AL.

academics, practitioners, and policymakers alike. By enhancing the under-
standing of factors that contribute to small business endurance or failure, our
study aims to foster a more sustainable ecosystem for small businesses, crucial
engines of economic growth and innovation.
Theoretical framework
This research investigates the predictors of viability among financially dis-
tressed small businesses by integrating insights from three distinct research
domains: small and medium-sized enterprise (SME) failure prediction, busi-
ness turnaround, and entrepreneurial resilience. Each domain is aligned with
the objectives of this study, concentrating on the unique challenges that small
businesses encounter during financial crises. Following brief overviews of
these three domains, the study synthesizes the most frequently cited indicators
of business failure derived from these areas. This integrative approach
enhances the understanding of the multifaceted nature of small business fail-
ure as well as the interdependencies among the domains.
SME failure prediction and the essence of integrating qualitative variables
Historically, the domain of SME failure prediction has concentrated on asses-
sing financial risks and default likelihood for credit applications, highlighted
by Edward Altman’s seminal Z-Score model in the 1960s (Altman, 1968).
However, various authors claim that its application to small businesses is
limited due to the sensitivity and unreliable nature of their financial data
(Ciampi & Gordini, 2013; Ciampi et al., 2021; Keasey & Watson, 1986,
1987). Others mention that the financial performance of small businesses is
often too volatile to be effectively represented by historical financial ratios
(Ciampi, 2018; Edmister, 1972; Lukason & Laitinen, 2019). These limitations
underscore the inadequacy of traditional financial ratios as sole predictors of
small business failure. Lussier (1995) was one of the first to develop and test
a non-financial prediction model with 15 qualitative variables and led the way
in integrating qualitative variables in SME failure prediction models (Lussier,
1995). Comprehensive systematic reviews have mapped the evolving land-
scape of this research domain, highlighting the shift toward integrating both
financial and qualitative predictors in the assessment of small business viabi-
lity (Cheraghali & Molnár, 2023; Ciampi et al., 2021).
Business turnaround and the essence of dynamics
The second research domain, business turnaround, is building on Pearce and
Robbins’ two-stage turnaround response model and its extension by Trahms
et al. (Pearce & Robbins, 1993; Trahms et al., 2013). These models translate
JOURNAL OF SMALL BUSINESS MANAGEMENT 3

external and internal causes of decline to response factors on managerial
cognition, strategic leadership, and stakeholder management, and subse-
quently into actions at strategic and operational levels. These frameworks
emphasize the importance of reductions in costs and assets to restore profit-
ability while simultaneously investing resources to strengthen the firm’s mar-
ket position (Trahms et al., 2013). Recent research has bridged SME failure
prediction modeling with turnaround strategies (Altman et al., 2024), while
other authors have underscored the significance of entrepreneurial orientation
and managerial experience as vital contributors to effective turnaround stra-
tegies (Mayr et al., 2017; Vedy et al., 2021).
The consideration of dynamics is pivotal in business turnaround processes
(Abebe et al., 2011; Adriaanse et al., 2016; Blatz et al., 2006; Laitinen, 2008;
Trahms et al., 2013). The assumption in many business failure prediction
models is that the underlying failure process remains stable over time, which
is a significant discrepancy from reality (Balcaen & Ooghe, 2006). Others
demonstrate the importance of acknowledging different firm failure paths
(Lukason & Laitinen, 2019). Acknowledging and integrating temporal
dynamics into the present research will provide a more nuanced understand-
ing of how and why small businesses fail or survive.
A relevant literature stream in this domain is connected to the rise of
voluntary or out-of-court restructuring options in many countries, with the
aim of making financial restructuring accessible for small businesses. This new
legislation led, in various countries, to an increase in firm survival rates and
lower legal costs (Srhoj et al., 2023), and these procedures provide useful data
on businesses in financial distress that go into either direct liquidation,
successful restructuring, or failed restructuring. This literature stream delivers
insights into the predictors of each of these three outcomes (Collett et al., 2014;
Routledge & Gadenne, 2004) and the impact of a delay in restructuring
procedures (Srhoj et al., 2023). In the Netherlands, a similar out-of-court
restructuring option, the Whoa, was implemented in 2021 (Kroezen, 2023).
Entrepreneurship and the essence of entrepreneurial resilience
The third research domain is entrepreneurial resilience, involving the ability of
entrepreneurs to withstand disruptions, adapt to changing circumstances, and
recover from failures (Gianiodis et al., 2022; Hartmann et al., 2022; Korber &
McNaughton, 2018; Leonelli et al., 2024). The entrepreneur’s role constitutes
a crucial, until recently underexplored, dimension in business failure research
(Ciampi & Gordini, 2013; Corner et al., 2017; Morris et al., 2013). The
COVID-19 crisis, however, triggered a research stream on the role of entre-
preneurial characteristics on survival in extremely adverse conditions
(Hartmann et al., 2022; McGee & Terry, 2024; Sharma et al., 2024). Various
studies demonstrate that the individual resilience and persistence of the
4H. DIEPERINK ET AL.

entrepreneur have a positive effect on business survival. Entrepreneurs affect
the resilient responses of their firms through their traits (such as optimism),
capabilities (such as social skills), and behaviors (such as quick decision-
making), which contribute directly to the firm’s performance in the face of
adversity. They also manage the key vulnerabilities of the firm, which con-
tributes to its organizational resilience (Leonelli et al., 2024). Persistence refers
to the determination, resilience, and continuous efforts exhibited by entrepre-
neurs and is essential to stay focused, motivated, and committed, even in the
face of adversity (Caliendo et al., 2020; DeTienne et al., 2008; Isichei et al.,
2024).
Small businesses thrive on the entrepreneur’s ability to navigate challenges
creatively, underscoring the “personal” essence of small firms’ structure and
operations (Ciampi & Gordini, 2013; Doz & Kosonen, 2010; Morrison et al.,
2003). This entrepreneurial spirit, characterized by a unique mix of honesty,
ethical conduct, and relationship-building skills, significantly influences busi-
ness viability (Gonçalves et al., 2016). This flexibility, however, also exposes
small firms to higher risks of acute failures, often undetectable until imminent
(Lukason & Laitinen, 2019). Sarasvathy’s effectuation theory claims that sur-
viving entrepreneurs excel in uncertain environments by leveraging available
means, accepting affordable losses, forming strategic partnerships, and capi-
talizing on unforeseen opportunities (Sarasvathy, 2001). Thus, from the lit-
erature in this rising research domain of entrepreneurial resilience, it follows
that entrepreneurial behavior has a significant impact on the failure or survival
of small businesses.
Synthesis of potential predictors from research domains
Drawing from the three research domains outlined previously, numerous
independent variables have been recognized in previous research as potential
predictors of business failure. We categorize these into four clusters: financial
indicators, firm-related variables, managerial control variables, and entrepre-
neurial variables.
Financial indicators
The first cluster of failure predictors contains financial indicators, particularly
financial ratios, grouped into three fundamental categories: liquidity, profit-
ability, and leverage (Altman et al., 2020; Ciampi & Gordini, 2013; Jairaj et al.,
2014). Leverage indicators appear to be stronger predictors of SME failure
compared to liquidity and profitability ratios (Modina & Pietrovito, 2014).
Since its inception in 1968, the Altman Z-Score, which is a composite measure
derived from five financial ratios, along with its subsequent iteration, the
Altman Z’’-Score, which incorporates four ratios, has significantly influenced
business failure prediction methodologies. Despite numerous critiques over
JOURNAL OF SMALL BUSINESS MANAGEMENT 5

the years challenging the robustness, the Altman Z-Score and its derivatives
remain extensively utilized as either principal or supplementary tools for
default predictions (Altman & Sabato, 2007; Altman et al., 2017). In research,
related to insolvent businesses with an urgent need to restructure debt,
indicators of liquidity, profitability, leverage, equity, and size (measured in
assets) appeared to be significant in predicting the success of the restructuring
process (Collett et al., 2014; Routledge & Gadenne, 2004).
Additionally, a distinct strand of research has explored payment behavior,
focusing particularly on variables that capture payment delays and distur-
bances. This line of inquiry has proven significant, providing additional, non-
redundant information that enhances the predictive capability beyond that
achieved by financial ratios alone (Back, 2005). This enhancement in predic-
tion power, particularly pertinent to SMEs, has been empirically validated in
multiple studies (Ciampi, 2015; Ciampi et al., 2018; Norden & Weber, 2010).
Firm-related indicators
Research consistently underscores the significance of firm-related variables,
the second cluster of failure predictors, organized into control variables and
firm dynamics. First, firm-related control variables such as sector, size, and age
are frequently used within small business failure studies in order to ascertain
the influence of external and internal factors on business outcomes. Prior
research indicates that the specific sector in which a business operates plays
a substantial role in determining its performance and probability of failure
(Halabi & Lussier, 2014). Furthermore, empirical studies have identified both
age and firm size as significant predictors of SME failure, suggesting that
smaller, younger firms are at a higher risk of default (Abdullah et al., 2019;
Lugovskaya, 2010; Wang & Guedes, 2024). Research on the firm age–mortality
relationship concludes that deficiencies in management are significant pre-
dictors of failure for young firms, while a poor ability to adapt to market
developments is the prime failure predictor for established firms (Thornhill &
Amit, 2003).
Variables related to firm dynamics are critical, as they encapsulate a firm’s
performance relative to its competitors. This includes market developments
influenced by business-cycle-related variables and credit conditions (Filipe
et al., 2016). Additionally, a firm’s market position relative to its competitors
plays a pivotal role in its ability to navigate challenges and recover competitive
standing, which is essential for any turnaround strategy (Trahms et al., 2013).
The internal characteristics of the firm also have profound impacts on its
survival prospects during distress (Pearce & Robbins, 1993; Trahms et al.,
2013). This includes the ability of cost-cutting and flexibility in cost structure,
which allows for adaptability in changing market conditions; employee loyalty,
which sustains operational stability (Smondel, 2018); and the ability for orga-
nizational learning, which facilitates continuous improvement and innovation
6H. DIEPERINK ET AL.

(Hsu & Fang, 2009). Moreover, intellectual capacity (Cohen & Kaimenakis,
2007) and the ability to adopt an ambidextrous approach (Lubatkin et al.,
2006) ensure that firms maintain a balance between retrenchment and strate-
gic actions, which is crucial for promoting effective turnaround strategies.
A final aspect of firm dynamics encompasses the network ties of the firm.
This captures the firm’s relationships with customers and suppliers, which are
crucial for a firm’s resilience and its ability to respond effectively to market
demands (Chen et al., 2013; McCann & McIndoe, 2015). These network ties
extend beyond immediate transactional interactions, influencing the firm’s
strategic flexibility and access to resources in critical times. Additionally,
cooperation networks, which involve alliances and partnerships across differ-
ent sectors, significantly contribute to a firm’s competitive edge and sustain-
ability (Zeng et al., 2010). Home-based networks, which relate to a firm’s local
community ties and regional affiliations, also play a pivotal role, particularly
for businesses that rely on local markets and community support (Zhou et al.,
2007). These network ties collectively support the ability to weather economic
downturns and emerge competitively post-crisis. Many family firms appear to
have relatively strong network ties, likely to enhance these firms’ survival
(Freixanet et al., 2024).
Managerial control related indicators
The third cluster of small business failure predictors is related to managerial
control. Argenti’s seminal work identifies a failure trajectory where internal
defects escalate into fatal managerial errors, culminating in visible signs of
business failure (Argenti, 1976). He categorizes defects into three main areas:
managerial incompetence, inadequate accounting systems, and resistance to
change. Since Argenti, numerous researchers investigated management con-
trol variables in the context of business failure, which could be categorized into
management structures, professionalism, planning and control systems, and
level of resources.
Management structures cover variables on governance. Director turnover,
membership of female or family directors, CEO duality, and concentration of
ownership appear to be negatively related to failure (Altman et al., 2016, 2022;
Ciampi, 2015; Freixanet et al., 2024; Laitinen & Gin Chong, 1999; Wilson &
Altanlar, 2014). Exit of key members is positive related to business failure
(Altman et al., 2022; Ciampi, 2015). For family businesses, concentration of
ownership appears to have a mixed effect on survival. It is proven to be
effective for the allocation of internal resources and ineffective for the mobi-
lization of external resources (Freixanet et al., 2024).
The second group in this cluster, managerial control professionalism, is
captured by levels of management skills, education, expertise, and experience
(Coff, 1997; Collett et al., 2014; Grunert & Norden, 2012; Halabi & Lussier,
2014; Lussier, 1995; Lussier & Pfeifer, 2001; Thornhill & Amit, 2003).
JOURNAL OF SMALL BUSINESS MANAGEMENT 7

Management incompetence appears to be an important early warning signal,
followed by deficiencies in accounting systems and attitudes toward customers
(Laitinen & Gin Chong, 1999). Trahms et al. (2013) add managerial cognition
(awareness of decline, attribution of decline, perception of decline severity),
strategic leadership, and stakeholder management (Trahms et al., 2013).
Lussier finds that involving skilled advisors contributes to preventing business
failure (Lussier, 1995). Some authors address the dark side of management
control with fraud and bad payment behavior by CEOs/directors and show
that this behavior drives firm failure (Back, 2005; Kallunki & Pyykkö, 2013).
A third group of managerial control indicators covers the effectivity of
planning and control systems (Halabi & Lussier, 2014; Laitinen & Gin
Chong, 1999). It also includes the presence of a management plan (Lussier,
1995) and the availability of audit report disclosures (Altman et al., 2016;
Camacho-Miñano et al., 2024; Muñoz-Izquierdo et al., 2019).
A final group of managerial control indicators covers the risk of lack of
resources. It includes the ability to acquire critical levels of working capital
(Halabi & Lussier, 2014; Lussier, 1995) and staffing (Lussier, 1995). Altman
et al. find that the employee firing ratio is a statistically significant predictor as
well (Altman et al., 2022).
Entrepreneurial indicators
The fourth and final cluster in the quest for predictive variables is related to the
role and contribution of the entrepreneur in the context of business failure or
survival, including the ability of entrepreneurs to show resiliency and persis-
tency in order to withstand disruptions, adapt to changing circumstances, and
recover from failures (Korber & McNaughton, 2018).
Relevant entrepreneurial resilience factors could be categorized into beha-
vioral, relational, and psychological factors. Behavioral indicators include the
level of self-efficacy, a key element of resilience (Hartmann et al., 2022; Korber
& McNaughton, 2018; McGee & Terry, 2024), as well as pro-activeness,
acceptance of affordable loss, readiness to experiment, and embracing failure
as part of a learning process (Artinger & Powell, 2016; Branicki et al., 2018;
Fatoki, 2018; Iwan Donal Paska & Eko, 2022; Salisu et al., 2020; Sarasvathy,
2001). Relational indicators include the ability to acquire social capital through
supportive relationships and professional networks (Artinger & Powell, 2016;
Branicki et al., 2018; Iwan Donal Paska & Eko, 2022; Sarasvathy, 2001).
Finally, psychological indicators include the ability of the entrepreneur to
manage stress and coping skills (Artinger & Powell, 2016). They also address
health issues as indicators for failure or survival. Entrepreneurs experience
30 percent more depression compared to non-entrepreneurs (Cubbon et al.,
2020; Freeman et al., 2019; Schonfeld & Mazzola, 2015). Mental health issues can
impact the way entrepreneurs function socially, physically, and cognitively,
decreasing their ability to perform effectively and undermining the self-efficacy
8H. DIEPERINK ET AL.

of the entrepreneur (Hessels et al., 2018). Factors that are mentioned in the
literature that influence mental health are: responsibility for liabilities, long
working hours, poor sleep, financial and personal failure, isolation, lack of social
support, divorce and relationship strain, and fear of stigma (Cubbon et al., 2020).
Hypothesis formulation
The purpose of the present research is to better understand the indicators for
assessing viability of small businesses in a state of financial distress.
Hypotheses 1–4 are related to the statistical significance of different clusters
of indicators, while the fifth hypothesis states that the combination of different
predictor categories significantly enhances the predictive power relative to
single predictor cluster models.
H1: Financial indicators alone are not sufficient to predict business failure in
financially distressed small businesses, as demonstrated by non-significant
p-values and low predictive accuracy metrics such as area under the curve
(AUC) values.
H2: Firm-related variables do significantly predict business failure in finan-
cially distressed small businesses, as demonstrated by significant p-values and
acceptable predictive accuracy metrics such as AUC values.
H3: Managerial control variables do significantly predict business failure in
financially distressed small businesses, as demonstrated by significant p-values
and acceptable predictive accuracy metrics such as AUC values.
H4: Entrepreneurial variables do significantly predict business failure in
financially distressed small businesses, as demonstrated by significant p-values
and acceptable predictive accuracy metrics such as AUC values.
H5: A combined model incorporating financial, firm-related, managerial
control, and entrepreneurship variables significantly enhances the predictive
accuracy for business failure among financially distressed small businesses
over single predictor category models, as demonstrated by statistically signifi-
cant improvements in model metrics.
Research design and methodology
The data set
This study utilizes a data set comprising 520 Dutch small businesses facing
financial distress in the period 2011–2019, identified through their
JOURNAL OF SMALL BUSINESS MANAGEMENT 9

application for aid under the so-called Bbz arrangement, a government
program aimed at financial restructuring and support for small businesses
in financial distress. The eligibility conditions for this support are: a) the
entrepreneur’s income has decreased below the social welfare level; b) there
is a credit rejection from a commercial bank; and c) the firm is assessed as
viable by an independent expert. Most of those viability audits are per-
formed by the Dutch Institute for Small and Medium-sized Enterprises
(IMK). The Dutch landscape of small businesses presents an ideal context
for this study, given the tight integration of these enterprises into both local
and international markets. This characteristic mirrors trends observed
across Europe, where small businesses represent more than 90 percent of
all businesses. The Dutch government’s proactive support for small busi-
nesses through various policies further underscores the relevance of this
setting for examining small business failure and resilience.
The viability audit process
The purpose of a viability audit for a firm is to assess whether or not the firm is
likely to generate free cashflows that are sufficient to structurally cover finan-
cial expenses, investments to assure continuity, and a minimum acceptable
entrepreneurial income. The viability audit includes an assessment of the
options to restructure the firm in order to create sustainable viability. The
viability audit thus has a wider scope than a financial audit and offers a rich
source of both financial and qualitative data, and insights in the context of the
cases in question.
Each audit commences with the collection of financial and market data,
leveraging the comprehensive benchmark and trend resources provided by
IMK. This stage is followed by a detailed interview by a qualified auditor with
the business owner, focusing on the context of the distress encountered by the
firm. During this interview, both internal and external causes of decline are
identified. Strategic and operational response actions are then explored in
collaboration with the business owner, taking into account the environment
of the firm and the owner’s personal circumstances. Following this initial
screening phase, the auditor conducts a stepwise validation process, which
includes a detailed analysis of financials, sector developments, market posi-
tioning, managerial control, and entrepreneurial competencies, as well as the
necessary response actions to assure the continuity of the business. During the
financial analysis, the auditor normalizes financial information for missing
values, outliers, and one-off transactions. The temporal dimension is crucial in
assessing firm viability, requiring auditors to determine the direction of
impact of each relevant observation, whether it increases or decreases future
cash flows. These impacts are categorized as recovery factors, which enhance
cash flows, or distress factors, which diminish them. It is important to note
that various observations may have contradictory effects on future cash flows,
10 H. DIEPERINK ET AL.

presenting a complex dynamic distinct from that in financial audit reports
where disclosures typically signal increasing risks for the firm (Muñoz-
Izquierdo et al., 2020).
Upon completing the analysis and validation phases, the auditor proceeds
to synthesize the findings into projected financial forecasts for the next three
to five years. This projection includes detailed estimates of future cash flows
and the firm’s capacity to meet its financial obligations from these cash flows.
The culmination of this process is the auditor’s overall judgment on the firm’s
viability, which is documented in a comprehensive viability audit report.
Mitigating cognitive biases in viability audits
Expert judgment forms the cornerstone of the viability audits utilized in this
study. Previous research on using audit report disclosures and key audit
matter disclosures in assessing the probability of business failures proves the
predictive ability of financial audit reports from accountants, which supports
the predictive ability of viability audits by experts (Camacho-Miñano et al.,
2024; Muñoz-Izquierdo et al., 2019).
Cognitive biases, as detailed by Broekema (Broekema, 2020), present poten-
tial pitfalls in the audit process. Anchoring bias, where initial information
unduly influences judgment; similarity bias, which favors information from
likeminded individuals (Gardner et al., 2013); outcome bias, an error in
evaluating decisions based on known outcomes (Baron & Hershey, 1988);
and engagement bias, a preference for decisions benefiting the client
(Broekema, 2020) all threaten the objectivity and accuracy of viability assess-
ments. Conversely, the so-called situativity theory emphasizes the balance
between specific knowledge and the context within which it is applied
(Holmboe & Durning, 2020). In medical diagnostics, this theory has high-
lighted the importance of integrating professional knowledge with the
dynamic context of each case to enhance diagnostic accuracy. Applied to
business viability audits, this theory suggests that auditors, armed with
detailed content knowledge from financial and commercial analyses, enriched
by the contextual understanding gained through interactions with entrepre-
neurs and direct observations, are uniquely positioned to make informed
judgments and potentially mitigate the risks introduced by cognitive biases.
To further minimize the risk of cognitive biases, each viability audit report
undergoes a rigorous quality assurance review by IMK, reflecting a structured
yet expedient approach to determining the viability of a business. The entire
viability audit process is conducted over a period of two to four weeks.
Sample and data compilation
The audits came from 777 limited liabilities enterprises of which 260 were
deemed viable and 517 non-viable. From the non-viable set, 260 were ran-
domly selected, creating a mirroring set of 260 viable and 260 non-viable
JOURNAL OF SMALL BUSINESS MANAGEMENT 11

audits for detailed analysis. The audits yielded 46 distinct data points per
business, encompassing financial metrics, firm attributes, and qualitative
insights into firm dynamics, managerial control, and entrepreneurial tenden-
cies. The status of each business was tracked up to three years post-audit to
ascertain its survival or failure, providing a robust foundation for analysis.
Dependent and independent variables
We have defined our dependent variable, business survival, as a business
remaining active for at least three years following a situation of financial
distress. This approach is in line with previous research, and the definition
in the present research is both valid and reliable (Collett et al., 2014; Liou &
Smith, 2007; Pacheco, 2015). A firm is defined as “active” if it is registered as
such in the Dutch Chamber of Commerce and actively presents itself to its
market. Conversely, small business failure is defined as a business that is no
longer active for three years following a financial distress situation.
The independent variables in this study are divided into indicators related
to financial ratios, firm dynamics, managerial control, and entrepreneurial
characteristics. To effectively capture the dynamics, each non-financial vari-
able is represented by two distinct dummy variables, designed to reflect
positive and negative impacts on future cash flows, as noted in the auditor’s
viability audit report. If the auditor determines that an observation is likely to
adversely affect future cash flows, it is classified as a distress factor, and the
corresponding distress dummy variable is assigned a value of 1. Conversely, if
an observation is deemed to positively influence future cash flows, it is
identified as a recovery factor, and the associated dummy variable is assigned
a value of 1. This approach allows for a nuanced analysis of the variables’
impacts, facilitating a more accurate assessment of their effects on the firm’s
viability.
Financial indicators
In this study, the financial indicators are taken from the Altman’s Z’’‐
Score model due to its applicability to SME firms, their frequency in
research literature on SME failure prediction, and efficacy according to
prior literature (Altman, 1983; Altman et al., 2017; Balcaen & Ooghe,
2006; Bellovary et al., 2007; Tong & Serrasqueiro, 2021). This model is
composed of four financial ratios, reflecting the liquidity, solvency,
profitability, and leverage of the firm. These are working capital to
total assets (WCTA), retained earnings to total assets (RETA), earnings
before interest and taxes to total assets (EBITTA), and book value of
equity to total liabilities (BVETL) (Altman et al., 2017). A low liquidity
ratio, WCTA, expresses liquidity issues. A low solvency ratio, RETA,
displays long‐term profitability issues and signals risk of bankruptcy.
12 H. DIEPERINK ET AL.

The profitability ratio, EBITTA, represents the productivity and effi-
ciency of the firm, and a decrease in book value of equity to total
liabilities, BVETL, indicates that the share of equity holders in the
economic value of the firm decreases, due to the total liabilities owed
to third parties. In the present research, financial indicators related to
payment behavior are not included, despite their relevance, as shown in
previous research, due to a lack of information on this topic in the
viability audit reports in our data set. Financial indicators are summar-
ized in Table 1.
Firm dynamics variables
Firm dynamics variables are organized into market developments, market
position, cost structures, customer dependencies, and stakeholder
dynamics. Each variable is represented by a dummy distress factor and
a dummy recovery factor, jointly describing the dynamics that directly
influence the performance of the firm. Additionally, control variables for
sector, size, and firm age are incorporated to ascertain the influence the
external and internal factors have on the firm’s performance. The firm
dynamics variables for the present research are summarized in Table 2
with a link to indicators from previous research, as mentioned in the
theoretical framework section.
Managerial control variables
The second cluster of non-financial variables contains managerial control
variables, organized into the level of control and investment issues. These
variables too are each represented by a dummy distress factor and a dummy
recovery factor. The variables included in the present research are summarized
in Table 3 with a link to indicators from previous research.
Entrepreneurial variables
The third cluster of non-financial variables contains entrepreneurial variables,
divided into personal risk factors and personal finance issues. These variables
are each represented by a dummy distress and a dummy recovery factor. The
variables cover behavioral, relational, and psychological factors, as mentioned
in previous literature. The variables included in the present research are
summarized in Table 4 with a link to indicators from previous research.
Table 1. Definition of financial variables.
Financial ratio Definition Code
Liquidity (Current Assets—Short-term Liabilities)/Total Assets WCTA
Solvency Retained Earnings/Total Assets RETA
Leverage Equity Book Value/Total Liabilities BVETL
Profitability EBIT/Total Assets EBITTA
Z’’−Score Z’’ = 3.25 + 6.56*WCTA +3.26*RETA +6.72*EBITTA +1.05*BVETL Z2
JOURNAL OF SMALL BUSINESS MANAGEMENT 13

Statistical methodology
The statistical methodology for variable selection and analysis deployed in this
study evaluates the predictive power of a broad array of variables concerning
binary small business financial distress outcomes, business survival or failure,
defined as the firm being active or non-active in business, three years post-
audit. The level of statistical significance for this study is set on p < .05.
Commencing with a list of 43 predictor variables, including five financial
ratios, 21 qualitative factors delineating distress and recovery, alongside 17
control variables, the analysis undertook a methodical approach to refine and
validate the predictive model.
Variable selection and reduction
Employing the least absolute shrinkage and selection operator (LASSO) tech-
nique, a grounded method was applied to mitigate the risk of overfitting by
selectively compressing the influence of less significant predictors. This pro-
cess ensured, by incorporating a variance penalty, an optimal balance between
model complexity and predictive accuracy. The study’s substantial dataset of
520 cases against the backdrop of 43 variables provided a robust foundation
for employing the LASSO method effectively.
Logistic regression analysis
After variable selection and reduction, logistic regression was executed, lever-
aging the distilled variable set to forecast business outcomes. This binary
logistic regression, aligning with the binary nature of the study’s dependent
Table 2. Description of firm dynamics variables and link with theoretical framework.
Group
In present
research
Variables from
theory Selection of papers
Market developments Market
developments
Business cycle
indicators
Lussier (1995), Filipe et al. (2016)
Credit conditions Filipe et al. (2016)
Unemployment
rates
Filipe et al. (2016)
Market position Market position Product
differentiation
Product/Service
timing
Trahms et al. (2013)
Lussier (1995)
Price differentiation Trahms et al. (2013)
Cost structures Cost dynamics Cost cutting ability Trahms et al. (2013), Collett et al. (2014)
Cost flexibility Trahms et al. (2013), Collett et al. (2014)
Network ties Customer
dependency
Customer ties Chen et al. (2013), McCann and McIndoe
(2015)
Stakeholder
dynamics
Cooperation
networks
Zeng et al. (2010)
Home-based
networks
Lussier (1995), Zhou et al. (2007), Freixanet
et al. (2024)
Firm-related control
variables
Size Size Collett et al. (2014), Routledge and Gadenne
(2004)
Sector Sector Halabi and Lussier (2014)
Firm age Firm age Thornhill and Amit (2003)
14 H. DIEPERINK ET AL.

variable (business survival or failure), produced probability scores indicative
of each business’s failure or survival likelihood (Altman et al., 2010). Logistic
regression is particularly advantageous in comparison to alternative analytical
techniques, such as machine learning, when the dataset is relatively small or
when the number of predictors is limited. It provides a straightforward inter-
pretation of coefficients, which is crucial in this study to understand the
impact of specific variables on the likelihood of business failure. This applic-
ability facilitated a nuanced analysis across diverse predictors, culminating in
the development of seven distinct predictive models. The statistical signifi-
cance of each predictor was tested using p-values derived from these regres-
sion models. These p-values are summarized in Table 8, using the *-notation
for significance classes. The p-values of all significant variables (p < .05) are
reported in the text in the results section.
Each model’s efficacy was assessed through the area under the curve (AUC)
metric, indicative for the combination of sensitivity and specificity of
Table 3. Description of managerial control variables and link with theoretical framework.
Group
In present
research
Variables from
theory Selection of papers
Management
structure
Level of control Governance Lussier (1995), Ciampi (2015)
Ambidextrous
behavior
Lubatkin et al. (2006)
Director turnover Ciampi (2015), Altman et al. (2022), Laitinen and
Gin Chong (1999)
Ownership
concentration
Freixanet et al. (2024)
Key member exits Altman et al. (2022), Ciampi (2015)
Professionalism Management skills &
education
Lussier (1995), Halabi and Lussier (2014), Grunert
and Norden (2012), Coff (1997)
Experience Lussier (1995), Halabi and Lussier (2014), Grunert
and Norden (2012), Coff (1997)
Managerial
cognition
Trahms et al. (2013)
Strategic leadership Trahms et al. (2013)
Stakeholder
management
Trahms et al. (2013)
Use of skilled
advisors
Lussier (1995)
Fraudulent behavior Back (2005), Kallunki and Pyykkö (2013)
Effective planning &
control systems
Deficiencies in
accounting
systems
Lussier (1995), Laitinen and Gin Chong (1999)
Quality, accessibility,
use
Halabi and Lussier (2014), Laitinen and Gin
Chong (1999)
Presence
management plan
Lussier (1995)
Presence
restructuring plan
Collett et al. (2014), Routledge and Gadenne
(2004)
Audit report
disclosures
Altman et al. (2016), Camacho-Miñano et al.
(2024), Munoz-Izquierdo et al. (2019)
Adequate resources Working capital Lussier (1995), Halabi and Lussier (2014)
Staffing Lussier (1995)
Employee firing ratio Altman et al. (2022)
Investment issues Investment
retrenchment
Retrenchment Trahms et al. (2013)
JOURNAL OF SMALL BUSINESS MANAGEMENT 15

a prediction model, spanning from 0 (entirely inaccurate) to 1 (perfect accu-
racy), with 0.5 denoting a performance no better than chance. AUC provides
a measure of how well the model distinguishes between failing and non-failing
businesses, independent of any specific classification threshold, and offers
a direct assessment of predictive accuracy, making it a robust measure for
our analysis objectives. AUC is particularly valuable in contexts where class
distribution is imbalanced or where the costs associated with different types of
misclassification vary significantly, which is relevant for business failure pre-
diction studies. AUC is widely recognized for its utility in evaluating binary
classification models. The AUC metric indicates acceptable prediction quality
with values above 0.7 (Paraschiv et al., 2021).
Besides the AUC metric, each model performance was assessed by two
additional performance measures: through the overall model p-value and
through the Akaike information criterion (AIC). The model p-value tests the
hypothesis that all coefficients in the model (except for the contant) are zero
and thus have no significant impact on the outcome. An overall p-value <.05
indicates statistical significance of the model. The AIC is an estimator of
prediction error and is applied for regression model comparisons. AIC
works by penalizing models that have more parameters, which tend to overfit
the data and reduce the predictive power of the model. Lower AIC values
indicate less prediction error.
For the comparison of prediction accuracy between different models, AUC
values from the different models were pairwise tested for significant differ-
ences in AUC value by conducting DeLong’s tests. This test provides a method
for statistically comparing the areas under two ROC curves derived from the
same cases. This determines whether the observed improvements in AUC are
statistically significant and not due to random chance.
Table 4. Description of entrepreneurial variables and link with theoretical framework.
Group
In present
research Variables from theory Selection of papers
Behavioral Personal risk Drive and pro-
activeness
Artinger and Powell (2016), Branicki et al. (2018), Korber
and McNaughton (2018)
Self-efficacy Artinger and Powell (2016), Branicki et al. (2018), Korber
and McNaughton (2018)
Focus on means at
hand
Sarasvathy (2021)
Embracing uncertainty Sarasvathy (2021)
Relational Building through
collaboration
Sarasvathy (2021), Artinger and Powell (2016), Branicki
et al. (2018)
Psychological Managing stress and
coping skills
Artinger and Powell (2016)
Managing health issues
Age and experience
Cubbon et al. (2020), Freeman et al. (2019), Schonfeld
and Mazzola (2015)
Lussier (1995)
Private
finance
High private
debt
n.a.
Private
withdrawals
n.a.
16 H. DIEPERINK ET AL.

Results
Descriptive statistics
The descriptive statistical analysis of this study illuminates the financial health
and viability outcomes of 520 Dutch small firms, spanning from 2011 to 2019,
amidst varying economic conditions. For each year the sample is mirrored
with equal positive and negative viability judgments. The temporal distribu-
tion of these cases, with a concentration of 35 percent audited during the
economic recovery period of 2011–2012 and a mere 8 percent in the relatively
stable economic environment of 2018–2019, reflects the lingering effects of the
2008 financial crisis. The dataset reveals a diverse array of business sizes and
sectors, with micro-businesses (less than €200,000 yearly revenue) represent-
ing 43 percent of the sample and 18 percent with yearly revenues, exceeding
1 million euro. Sixty-eight percent of firms were established for over five years.
The sample encompasses a broad cross-section of industries, from services
(41 percent) to transport (23 percent) and manufacturing (12 percent).
Financial health
Financial analysis, drawn from the audit reports, underscores a pervasive
condition of financial distress, evidenced by negative median equity and
slightly negative financial ratios across the board, highlighting the ubiquitous
challenge of financial instability among these small businesses, as summarized
in Table 5.
Distress and recovery factors
An examination of the non-financial factors within the audit reports, sum-
marized in Table 6, reveals a nuanced picture of distress and recovery factors,
providing a deeper understanding of the temporal dynamics affecting firm
performance and survival. A distress factor predicts a negative impact on
future cashflows and a recovery factor predicts a positive impact.
Viability judgement and prediction accuracy
Each viability audit report holds an overall judgment by the auditor on the
viability of the firm, at the audit date. The status of each firm was tracked up to
three years post-audit to ascertain its survival or failure. Table 7 describes the
Table 5. Statistical description of financial variables in audit reports.
Financial ratio Mean Min Q1 Median Q3 Max
Equity −45 −1,121 −126 −22 30 1,277
Liquidity −0.92 −102.00 −0.51 −0.09 0.16 1.00
Solvency −1.24 −103.00 −0.66 −0.11 0.15 1.00
Leverage 0.05 −92.00 −0.44 −0.11 0.19 118.00
Profitability −0.16 −8.33 −0.25 −0.05 0.03 17.00
Z’’−Score −7.77 −888.46 −4.97 1.59 4.44 134.85
JOURNAL OF SMALL BUSINESS MANAGEMENT 17

confusion matrix, the categorization of firms based on viability judgments and
subsequent three-year survival and offers a compelling view of the predictive
accuracy of these audits.
With 76 percent of firms deemed viable remaining active beyond three
years, revealing a type 1 error rate of 24 percent. These results lag just slightly
behind the Dutch national average survival rate. However, the data show
a notably high type 2 error rate of 52 percent, representing firms judged as
non-viable but remained active for at least three years post-audit. This high
error rate underscores the challenges in accurately predicting small business
resilience based on audit assessments alone. The audit process, conducted by
63 auditors, demonstrates a consistent distribution of prediction errors across
auditors, suggesting that the individual auditor’s influence on the outcome is
limited. The overall accuracy of the viability judgments is 62 percent, reflecting
the inherent difficulties in forecasting small business survival from a state of
financial distress.
Logistic regression analysis
This study employed logistic regression to analyze the predictive efficacy of
various models on small business financial distress outcomes, developing
Table 6. Occurrence of predictors for recovery or distress, as mentioned in viability audit
reports.
Mentioned in viability audit reports
Non-financial variables Recovery factor Distress factor
Firm dynamics 57% 29%
Market developments 29%* 5%*
Market position 10%* 12%
Customer dependency 8% 7%
Stakeholder dynamics 6% 8%
Cost dynamics 24%* 6%*
Managerial control 29% 25%
Level of control 13%* 8%*
Investment issues 14%* 14%*
Entrepreneurial characteristics 27% 59%
Personal risk 15%* 7%*
Private withdrawals 15%* 13%*
High private debt 0% 53%*
*significant and selected in LASSO procedure.
Table 7. Confusion matrix: audit judgment in year t and status in year t +
3.
Survived Failed
331 189
Positive audit 260 197 (76%) 63 (24%)
True Positive False Positive
Negative audit 260 134 (52%) 126 (48%)
False Negative True Negative
18 H. DIEPERINK ET AL.

seven distinct models through the examination of clusters of variables, ranging
from financial ratios to qualitative indicators of firm dynamics, managerial
control, and entrepreneurial characteristics, all split in recovery and distress
factors to include temporal dynamics in the variables into the logistic
regression.
Methodological approach
The initial model, serving as the baseline, incorporated four financial
ratios for liquidity, solvency, profitability, and leverage to predict busi-
ness viability, followed by our second model that utilized the Altman
Z’’-Score as the sole predictor. Subsequent models were developed to
explore the predictive power of each of the three clusters of variables
with built-in temporal dynamics: Model 3 focused on firm dynamics,
Model 4 on managerial control, and Model 5 on entrepreneurial char-
acteristics. Model 6 mixed the non-financial variables from Models 3, 4,
and 5, while Model 7 integrated the statistically significant variables
from these clusters along with the firm age dummy variable and
Altman Z’’-Score.
Analytical findings
The analysis, summarized in Table 8, revealed that the baseline model, with
financial indicators only, yielded minimal predictive accuracy, with an AUC
score of 0.515, barely exceeding that of random chance. Also, none of the four
financial indicators is statistically significant, as well as the overall model, with
a p-value of .231. A slight improvement in accuracy (0.567) was observed in
Model 2 when combining financial indicators in the Z’’-Score model, but this
financial indicator turned out to be non-significant as well. Also, the overall
model showed non-significance (p = .287). As a robustness check, we have
performed logistic regression using the three financial ratios of the Zmijewski
score model, commonly used in prior literature. These results are consistent
with our results above for the financial ratios and the Z’’-Score (Muñoz-
Izquierdo et al., 2020; Wu et al., 2010; Zmijewski, 1984). These findings
strongly support hypothesis (H1). This result is not unexpected, since all
firms in our sample are already in a state of financial distress, reducing the
differential predictive capacity of past versus present financial performance.
Model 3, based on firm-related variables, encompassing market develop-
ments, market position, and cost dynamics, is overall significant (p = .012) and
shows one, out of five, significant predictors, representing a continuing nega-
tive market outlook (p = .043). However, the model shows poor predictive
power (AUC is 0.539). Hypothesis (H2), is therefore just partly supported by
our findings. Firm dynamics clearly have a significant influence on the prob-
ability of encountering financial distress, but it seems a poor predictor for the
ability to bounce back to a healthy business.
JOURNAL OF SMALL BUSINESS MANAGEMENT 19

Table 8. Seven regression models: coefficients and prediction performance metrics. Baseline model is regression on four financial ratios.
Model
Baseline model Z’’−Score model Firm dynamics Managerial control Entrepreneurial characteristics All qualitative variables Significant variables
1 2 3 4 5 6 7
Financial indicators
Liquidity −0.09
Solvency 0.099
Leverage −0.017
Profitability −0.056
Z’’−Score 0.002 0.002
Recovery factors
Market development 0.404* 0.396*
Market position 0.494 0.346
Cost dynamics 0.307 0.207
Level of control 0.302 0.337
Investment issues 0.642** 0.658** 0.667**
Personal risk 0.302 0.325
Private withdrawals 0.224 0.227
Distress factors
Market development −0.877** −0.814* −0.923**
Cost dynamics 0.089 0.373
Level of control −0.556* −0.49 −0.660*
Investment issues −0.129 −0.01
Personal risk −1.082*** −0.940** −1.247***
Private withdrawals −0.331 −0.182
High private debt −0.433** −0.401**
Firm age 1.067***
Constant 0.596 *** 0.574*** 0.370*** 0.508*** 0.847*** 0.540** −0.003
Model metrics
Model p-value 0.231 0.287 0.0120 0.030 0.0006 0.0002 5*e-10
AUC 0.515 0.567 0.539 0.609 0.651 0.642 0.706
AIC 686 684 680 681 672 672 641
Nagelkerke R2 0.015 0.003 0.035 0.028 0.056 0.101 0.137
Significance levels ***< 0.01, **< 0.05, *< 0.1.
20 H. DIEPERINK ET AL.

In contrast, the managerial control model (Model 4) shows slightly better
prediction accuracy (0.609), and the overall p-value indicates the statistical
significance of the model (p = .030). Only one out of four predictors (recovery
from investment issues) is statistically significant (p = .032), moderately sup-
porting our third hypothesis (H3).
The entrepreneurial model (Model 5) outperformed the previous four with
an AUC of 0.651 (overall model p-value <.001). The model shows that
distressing personal risk issues significantly influence business outcomes
(p = .002) as well as high levels of personal debt (p = .023). The results are
supportive of our fourth hypothesis (H4).
Our final model (Model 7), which synthesized significant predictors across
non-financial clusters of variables, and included the firm age and Z’’-Score,
exhibited a substantial increase in predictive accuracy, achieving an AUC
score of 0.706, indicative of acceptable prediction quality. The model’s
p-value (<.001) indicates the significance of the overall model, and four out
of five predictor variables are statistically significant: recovery from invest-
ment issues (p = .030), distressing market outlook (p = .038), distressing per-
sonal risk issues (p = .007), and firm age (p < .001). For hypothesis 5 (H5), we
compared the predictive accuracies of Model 7 and the other models using the
DeLong test to evaluate pairwise differences in their AUCs (DeLong et al.,
1988). The test yielded p-values <.05 for Model 7 over Models 1, 3, 4, and 6,
indicating for each pair of models a statistically significant difference between
the abilities to discriminate between the predicted outcomes (DeLong et al.,
1988). Model 7 superior performance is underscored by a lower AIC score
(641) compared to the other models.
The logistic regression analysis underscores the critical importance of
entrepreneurial variables and firm age as predictors of the ability to evade
financial distress, which leads to questioning what the underlying attributes of
firm age are that contribute to the survival power of small businesses. By
extending beyond traditional metrics to include variables that capture the
complex realities of small business operations, the study reveals a pathway
toward more nuanced and accurate predictive models. These insights not only
contribute to the academic discourse on business failure prediction but also
offer practical implications for small business management and support
strategies.
Discussion
Our study aimed to refine the predictive models for small business
failures by examining viability assessments of small businesses amidst
financial distress. It contributes to the existing literature on business
failure prediction by expanding the scope of traditional models by
incorporating entrepreneurial resilience, positing these traits as key
JOURNAL OF SMALL BUSINESS MANAGEMENT 21

indicators of a firm’s potential to endure and emerge from crisis.
Moreover, our study integrates temporal dynamics from the business
turnaround domain, recognizing that the timing of disturbances or
interventions can significantly influence outcomes. This study is sup-
ported by a unique dataset comprising 520 viability audits of small
businesses, all encountering financial distress and thus on the edge of
failure. These audits include a comprehensive analysis of the business
and the entrepreneur, historical and predicted financials, and the con-
text of distress. Furthermore, each audit contains an overall statement
by the auditor categorizing the firm as viable or not.
Utilizing logistic regression analysis, our study confirmed that finan-
cial indicators lose their predictive accuracy once a business is in a state
of financial distress. The inclusion of indicators for managerial control
dynamics enhanced the predictive accuracy and further amplified the
integration of entrepreneurial indicators, underscoring their critical role
in assessing small business viability. These findings highlight the neces-
sity of incorporating dynamics in managerial control and entrepreneur-
ial characteristics into the viability assessments of small businesses
nearing the edge of failure.
Key ndings
On the edge of failure financial and firm indicators lose their predictive power
Prior research has consistently demonstrated that financial and firm-related
indicators serve as robust classifiers for distinguishing between bankrupt
and non-bankrupt businesses. Contrary to these findings, our research
shows that such indicators do not maintain their predictive strength
when applied to businesses already experiencing financial distress. While
financial and firm-related indicators are valuable in identifying the initial
trajectory from a healthy to a distressed state, they fail to accurately predict
the subsequent critical transition from financial distress to either recovery
or failure.
Our analysis of financial indicators, which yielded an AUC of 0.515, no
better than random chance, provides substantial support for our first hypoth-
esis, that financial indicators are not effective for failure prediction in the
context of distressed small businesses. More unexpectedly, our results show
that the inclusion of firm-related indicators does not significantly improve
predictive capability. These findings suggest that both financial and firm-
related variables exert minimal or no influence on a firm’s capacity to recover
from financial distress, highlighting the need for predictors that can more
accurately capture the nuances of business viability at critical stages in the
firm’s life cycle.
22 H. DIEPERINK ET AL.

Variables that undermine the entrepreneur’s persistence are strong predictors of
failure
Our investigation into Model 7 reveals that a diverse mix of variables emerge
as statistically significant predictors of failure among distressed small busi-
nesses. These include a distressing market outlook, inadequate internal control
mechanisms within the firm stemming from either insufficient information or
managerial incompetence, and challenging personal circumstances, such as
serious health issues or disruptions in private relationships. At first glance,
these variables seem uncorrelated, but we posit that these factors are each
capable of undermining the entrepreneur’s persistence, subsequently dimin-
ishing the firm’s resilience. This underscores the critical role of entrepreneur-
ial resilience in sustaining business viability.
Higher firm age allows development of resilience and is a predictor of business
survival
Our research supports our fifth hypothesis, identifying firm age as a statistical
significant predictor of business survival. Specifically, our findings indicate
that businesses older than six years exhibit greater resilience compared to
younger firms aged between two and six years, with startups excluded from
our analysis. We contend that the correlation between firm age and business
survival is strongly influenced by the cumulative development of entrepre-
neurial resilience factors over time, as delineated in our theoretical framework.
This evolution moves in several dimensions: development of managerial
competences, behavioral factors such as increased efficacy, relational factors
include enhancing social capital through networks, and psychological factors
like improved stress management and coping mechanisms. These findings
underscore the importance of temporal dynamics in business resilience, sug-
gesting that longevity contributes to a firm’s ability to survive.
Professional judgement and bias
An intriguing aspect of our findings is the discrepancy between professional
judgment at the audit date on expected viability and the actual outcomes three
years post-audit. Specifically, the auditors’ predictions were much more accu-
rate for failing businesses than for those that survived, with a surprisingly high
false negative error rate. More than 50 percent of businesses deemed non-
viable by auditors survived and remained active for at least three years post-
audit. This discrepancy may be attributed to cognitive biases in auditor
judgments, but we argue that, following the situativity theory, since the
auditors have detailed insight in the context of the situation, the risks of
cognitive biases should be mitigated. So we argue that the high false negative
error rate is likely caused by underestimating the impact of entrepreneurial
resilience.
JOURNAL OF SMALL BUSINESS MANAGEMENT 23

Theoretical implications
This article answers for the call for further studies in prior research on
enhancing failure prediction models with relevant qualitative variables
(Altman et al., 2014, 2022; Argenti, 1976; Ciampi et al., 2021; Lussier, 1995).
In particular, our study follows Ciampi and Gordini, who argue that entre-
preneurial characteristics should be included in small business failure predic-
tion to understand the underlying mechanisms of small business failure and
survival (Ciampi & Gordini, 2013).
With a focus on small businesses facing financial distress, our theoretical
framework is grounded in the integration of three key research domains: SME
failure prediction, business turnaround, and entrepreneurial resilience. This
multidimensional approach is not only vital for capturing the complex reality
of small businesses in challenging times but also for addressing the deficiencies
in current prediction models, which typically neglect the dynamic nature of
small business distress and the critical role of the entrepreneur. By integrating
these elements into our predictive models, we offer a more nuanced under-
standing of the factors contributing to the survival or failure of small
businesses.
The inclusion of entrepreneurial persistence and resilience brings a new
dimension to the predictive models, highlighting the personal influence of
business owners and their networks on the survival of their businesses. The
theoretical implications of our study advocate for a shift toward more dynamic
models, including entrepreneurial characteristics in small business failure
prediction. This shift is expected to improve the accuracy of predictions and
provide stakeholders with better tools for assessing and supporting small
businesses during critical periods.
Practical implications
Our findings challenge the efficacy of traditional financial indicators used by
financial institutions to assess distressed small businesses. We propose that
lenders integrate indicators on managerial control and entrepreneurial resi-
lience into their assessment frameworks. For that purpose, however, is further
research required related to the measurement of entrepreneurial resilience.
Some previous work on this topic has already been performed (Fatoki, 2018),
but to operationalize resilience in the context of business failure prediction,
additional effort is needed, similar to the operationalization of the Omega-
Score (Altman et al., 2023). This could significantly reduce the risk of loan
defaults and support business continuity by better identifying businesses with
recovery potential.
The significant role of entrepreneurial characteristics in business resilience
indicates that entrepreneurial education programs should extend beyond
24 H. DIEPERINK ET AL.

traditional business management and financial literacy. Incorporating training
on risk management, personal resilience, stress coping mechanisms, and turn-
around decision-making could profoundly benefit aspiring entrepreneurs,
equipping them to thrive even in adverse conditions. This education could
also benefit other stakeholders, such as accountants or other professionals,
involved in SME advisory services.
Our research also informs policy recommendations aimed at bolstering
small business support structures. Recognizing the societal value of viable
businesses, policies should be crafted to offer targeted support for entrepre-
neurs to enhance the personal and organizational resilience of small business
owners.
Our study underscores the need for stakeholder engagement by illuminat-
ing a broader set of variables that influence business outcomes. Investors,
suppliers, and customers can leverage this enriched understanding to make
more informed decisions, fostering stronger business relationships and a more
resilient small business sector. This is crucial for building a healthy business
ecosystem that nurtures long-term growth and financial stability.
Limitations and future research
While our study contributes significantly to the understanding of small busi-
ness failure prediction, it is not without its limitations. First, the scope of our
research was confined to small businesses within the Netherlands, which may
limit the generalizability of our findings to other geographical contexts,
although the Dutch landscape of small businesses presents an ideal context
for this study, given the significant integration of these enterprises into both
local and international markets. Different economic, cultural, and regulatory
environments could influence the applicability of our results in other regions.
Thus, future research should consider replicating this study in varied settings
to explore the generalization of the proposed models.
Second, our reliance on quantitative methods might have constrained our
ability to capture the nuanced impacts of entrepreneurial variables such as
entrepreneurial self-efficacy or personal stress factors, which are inherently
subjective and difficult to quantify. Future studies could benefit from incor-
porating qualitative methodologies, such as case studies or interviews, to gain
deeper insights into the qualitative aspects of entrepreneurial behavior.
Extending previous research on the measurement of entrepreneurial resilience
(Fatoki, 2018; Salisu et al., 2020) could be beneficial to operationalize the
present research.
Third, the classification of variables into binary categories of distress and
recovery factors may oversimplify the complex dynamics that small businesses
experience. This binary approach does not account for the possibility that
JOURNAL OF SMALL BUSINESS MANAGEMENT 25

certain factors could simultaneously have both positive and negative effects
depending on the context.
Building on the limitations highlighted, several avenues for future
research emerge. To address the issue of generalizability, subsequent
studies could apply the developed models to small businesses in differ-
ent countries, especially in those where voluntary or out-of-court
restructuring has been implemented with a build up of data sets that
have strong similarities to the data set used in the present research. This
expansion would not only test the robustness of the models across
diverse economic landscapes but also refine the predictive accuracy by
incorporating region-specific modifications.
Moreover, future research should explore integrating mixed-method
approaches to enrich the quantitative findings, particularly in the field
of entrepreneurial resilience. By combining quantitative data with quali-
tative insights obtained from first-hand narratives of entrepreneurs,
researchers can better understand how subjective factors like management
quality, entrepreneurial resilience, and personal life events influence busi-
ness outcomes. Since these factors include privacy sensitive data, future
research should explore data collection experiences in other research
domains. There is also a need for longitudinal studies that track the
performance of small businesses over extended periods. Such studies
could provide a clearer picture of how time-related factors, such as
economic downturns, calamities, and shifts in consumer behavior, affect
small business viability. This touches the elephant in the room, which is
whether, from societal and sustainability point of views, poor performing
businesses should be supported to restructure if those businesses are
expected to be viable.
In conclusion, our study marks a step forward in refining the tools available
for predicting small business failure, highlighting the importance of more
dynamic and integrative models, acknowledging the relevance of entrepre-
neurial resilience. Future research should continue to build on these founda-
tions, seeking to offer even more robust and contextually sensitive tools for
stakeholders involved in supporting the small business sector.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Han Dieperink http://orcid.org/0009-0003-6639-3441
26 H. DIEPERINK ET AL.

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