José Manuel Liñares-Zegarra and John O. S. Wilson, Navigating uncertainty: The resilience of third‐sector organizations and socially oriented small‐ and medium‐sized enterprises during the COVID‐19 pandemic (2023)
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Article by José M. Liñares-Zegarra of Essex Business School and John O. S. Wilson of the University of St Andrews in Financial Accountability & Management, accepted 8 June 2023, on how U.K. third-sector organizations and socially oriented SMEs weathered COVID-19. Using the Longitudinal Small Business Survey, the authors compare them with commercial SMEs. Social enterprises were less likely, and socially oriented SMEs more likely, than commercial firms to see the pandemic as an obstacle. Both groups were more likely to increase their activities. Third-sector organizations were less likely to use government-backed loans, which the authors read as a need for other forms of support.
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Received: 27 February 2023 Revised: 26 May 2023 Accepted: 8 June 2023
DOI: 10.1111/faam.12373
RESEARCH ARTICLE
Navigating uncertainty: The resilience of
third-sector organizations and socially oriented
small- and medium-sized enterprises during the
COVID-19 pandemic
José M. Liñares-Zegarra1 John O. S. Wilson2
1
Essex Business School, University of Essex,
Wivenhoe Park, CO4 3SQ, Colchester, UK Abstract
2
Centre for Responsible Banking & Finance, This paper investigates the impact of the COVID-19 pan-
School of Management, University of St
Andrews, St Andrews, UK
demic on U.K. third-sector (nonprofit organizations and
social enterprises) and socially oriented small- and medium-
Correspondence José M LiñaresZegarra Essex
sized enterprises (SMEs), and provides insights regarding
Business School University of Essex Wivenhoe
Park Colchester CO4 3SQ UK their organizational resilience. Using data from the Longi-
Email: jmlina@essex.ac.uk
tudinal Small Business Survey, the results of an extensive
Funding information empirical analysis suggest that relative to commercial (for-
ESRC-funded Enterprise Research Centre and profit) SMEs, social enterprises were less likely, and socially
the Department for Business, Energy and
Industrial Strategy (BEIS) oriented SMEs more likely to perceive the pandemic as an
obstacle to business success. Third-sector and socially ori-
ented SMEs were more likely to increase their activities
compared to commercial SMEs. Moreover, the COVID-19
pandemic appears to have had a differential impact on
the future plans of third-sector and socially oriented SMEs
relative to commercial SMEs. Third-sector organizations
were less likely to use government-backed loans, suggest-
ing a need for alternative forms of support or financing
to weather economic disruptions. Overall, our analysis sug-
gests a resiliency and versatility among third-sector and
This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and
reproduction in any medium, provided the original work is properly cited.
© 2023 The Authors. Financial Accountability & Management published by John Wiley & Sons Ltd.
Financial Acc & Man. 2023;1–26. wileyonlinelibrary.com/journal/faam 1
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2 LIÑARES-ZEGARRA and WILSON
socially oriented SMEs in dealing with unexpected and
significant external shocks.
KEYWORDS
COVID-19 pandemic, organizational resilience, small- and medium-
sized enterprises, social enterprises, third sector
1 INTRODUCTION
Third-sector organizations (TSOs; such as nonprofit organizations and social enterprises) and socially oriented small-
and medium-sized enterprises (SMEs) are crucial components of the U.K. business ecosystem. They play a vital role
in promoting sustainable and responsible business practices, and addressing ongoing societal challenges. In contrast
to mainstream commercial SMEs that focus solely on financial goals, TSOs and socially oriented SMEs pursue dual
social and financial goals.1 This study provides new evidence regarding the impact of the COVID-19 pandemic on the
behavior, resilience, and future plans of TSOs and socially oriented SMEs.
As a unique form of business, TSOs and socially orientated SMEs have attracted the attention of academics, prac-
titioners, and policy makers in recent years. These organizations operate across a wide range of industries, and are
characterized by a focus on addressing social and environmental challenges through innovative business models,
which strike a balance between making a profit and creating a positive social impact. Traditional nonprofit enter-
prises, also known as charitable organizations, are one of the most well-known types of organizations that operate
for the benefit of society, rather than simply for profit. These organizations typically rely on grants, donations, and
government funding to support their activities, which include providing essential services in areas such as healthcare,
education, and the arts. Social enterprises have also attracted special attention, given their dual mission of financial
sustainability and social purpose. Doherty et al. (2014) identify hybridity as the defining characteristic of social enter-
prises. These organizations are driven by a social purpose and reinvest any profits back into local communities. Given
their ability to integrate features of private, public, and nonprofit organizations, these hybrid organizations play a crit-
ical role in addressing societal issues; the provision of valuable services; and enhancing the resilience of the economy
(Liu & Ko, 2012; Murphy & Coombes, 2009). Socially oriented SMEs are for-profit businesses that prioritize social and
environmental responsibility in their operations. These businesses aim to balance financial goals with a commitment
to creating positive social and environmental impacts. Together, these three types of organizations play a significant
role in promoting sustainable business practices, addressing social and environmental challenges, and creating positive
outcomes for the economy and society.
While TSOs and socially oriented SMEs have demonstrated an ability to create positive social impact, they remain
vulnerable to sudden external shocks and crises that can threaten their very survival. For instance, the COVID-19
pandemic exposed the vulnerabilities of many social enterprises (hospitality and tourism industries) that rely on face-
to-face customer interactions. Given the significant disruptions brought about by COVID-19 (and the implementation
of various public policy interventions to slow the spread of the virus), it is important to understand the impacts on the
behavior and resilience of TSOs and socially oriented SMEs. Studying their organizational resilience not only fills an
important evidence gap, but also informs policy and practice toward effective responses to external shocks and crisis
events.
In order to investigate the impact of the COVID-19 pandemic on the behavior, resilience, and future plans of TSOs
and socially oriented SMEs, we rely on data compiled by the 2019–2020 Longitudinal Small Business Survey (LSBS)
commissioned and published by the U.K. Department of Business, Energy and Industrial Strategy2 (Department for
Business Energy and Industrial Strategy, 2022). The main advantage of using the LSBS as an information source is that
the sample of SMEs is representative of the population of 5.5 million U.K. SMEs and follows a consistent classification
methodology of firms based on their social and environmental goals.
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LIÑARES-ZEGARRA and WILSON 3
In order to provide a comprehensive understanding of the challenges and responses of TSOs and socially orientated
SMEs during the COVID-19 pandemic, our empirical research design utilizes probit and multinomial probit (MNP)
models across four areas. Specifically, we investigate (1) the extent to which TSOs and socially oriented SMEs view
COVID-19 as a major obstacle to business success; (2) the impact of lockdown restrictions on their business opera-
tions; (3) the impact of the pandemic on their future business plans; and (4) their use and access to U.K. Government
COVID-19 funding.
By way of preview, the results of our empirical analysis provide a number of important insights. First, social enter-
prises (socially focused SMEs) were less (more) likely to view the pandemic as a major obstacle to business success
compared to commercial SMEs. Second, TSOs and socially oriented SMEs were more likely to increase their activities
during the period where government-imposed lockdown restrictions were in force. Third, the pandemic had a differ-
ential impact on the future plans of TSOs and socially oriented SMEs compared to commercial SMEs. While traditional
nonprofits and socially oriented SMEs were less likely to need to adjust workforce development and leadership plans,
they were more likely to face significant challenges in continuing with plans for capital investment and recruitment
of new staff in overseas markets. Social enterprises, on the other hand, were less likely to have plans for introduc-
ing new working practices and developing and launching new products or services impacted by the pandemic, but
more likely to face challenges in executing plans for R&D investment, and selling to new overseas markets. Fourth,
traditional nonprofit and social enterprises were less likely to use COVID-19 government-backed accredited loans
or finance agreements compared to commercial SMEs. This suggests that traditional nonprofit and social enterprises
require alternative forms of finance to weather the economic disruptions caused by the COVID-19 pandemic. Socially
oriented SMEs were more likely to use COVID-19 business grants funded by government or local authority compared
to their commercial SME counterparts.
This paper makes several contributions to salient literature. First, our findings regarding the differential impact of
the COVID-19 pandemic on TSOs and socially oriented SMEs provide valuable insights to the role of organizational
structure in driving resilience, flexibility, and adaptability in business models in the face of unexpected challenges like
the pandemic (Bacq & Lumpkin, 2021; Belitski et al., 2022; Hyndman, 2020; Kober & Thambar, 2021; Magrizos et al.,
2021; Plaisance, 2022; Weaver, 2020; Weaver & Blakey, 2022). To the best of our knowledge, this is the first attempt to
explore this area from a multiorganizational perspective using a nationally representative sample of organizations and
businesses operating in the United Kingdom. Second, we contribute to the literature exploring the adaptability and
flexibility of TSOs and socially oriented businesses in the context of future strategic planning (Dickerson & Hassanien,
2018; Mathibe et al., 2023; Weaver & Blakey, 2022). The results on the impact of the pandemic on future business
plans highlight the need for TSOs and socially oriented SMEs to adapt their plans to ensure long-term viability in a
context of crisis management. Third, we contribute to the literature on the financing of organizations and businesses
with a social aim along with the need for targeted support during periods of economic uncertainty (Davies et al., 2019;
Doherty et al., 2014; Green et al., 2021; Lee & Cowling, 2013; Lyon & Owen, 2019; Pape et al., 2020). More precisely,
the empirical findings regarding the impact of the pandemic on future business plans suggest the need for specific
targeted support and interventions to help TSOs and socially oriented SMEs overcome the challenges posed by the
pandemic. Moreover, the results on the use of coronavirus COVID-19 government-backed accredited loans or finance
agreements suggest that TSOs and social enterprises may require alternative forms of support or financing to weather
economic disruptions arising from the pandemic. For example, targeted grants or subsidies, streamlined administrative
processes for accessing specific types of funding, or other forms of financial assistance could be more suitable for these
organizations.
Overall, the findings of this paper suggest that third-sector and socially oriented organizations have shown remark-
able resilience despite the challenges faced during the COVID-19 pandemic. Some TSOs and socially oriented SMEs
have successfully responded to changing needs and demands, while others have leveraged their own resources to
address challenges. The ability of social enterprises to adapt in response to crisis is important not only for survival,
but also for their potential to contribute to broader social and environmental goals. These results have important
implications for current and future policy toward organizations and businesses with a social mandate in the context of
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4 LIÑARES-ZEGARRA and WILSON
the COVID-19 pandemic (Bacq & Lumpkin, 2021). The findings also inform and guide stakeholders, including social
enterprises, investors, policy makers, and the public, in making informed decisions and supporting the growth and
impact of this important part of the U.K. SME ecosystem.
The rest of this paper is structured as follows. Section 2 examines relevant literature on the challenges for TSOs and
socially oriented SMEs during uncertainty periods, including COVID-19. Section 3 describes the data set used and the
research methodology. In section 4, we present the results of our empirical analysis and discussion. Section 5 presents
the main conclusions of the paper.
2 LITERATURE
This section reviews literature exploring the challenges faced by the TSOs and socially oriented SMEs during the
COVID-19 pandemic. The social enterprise sector in the United Kingdom contributes roughly 3% to the country’s
GDP, making it one of the fastest-growing forms of businesses. With over 100,000 organizations, the sector generates
£60 billion for the economy and provides employment to over 2 million individuals (Social Enterprise UK, 2018). Social
enterprises conduct a variety of commercial activities across economic sectors and contribute to job creation (Haugh
et al., 2022). The U.K. charity sector is crucial for the country’s well-being, there are over 200,000 registered chari-
ties with a combined estimated annual income of almost £80 billion, which employ 800,000 people and rely heavily on
volunteers (Hyndman, 2018, 2020). The sector provides a wide range of public services that in turn are a reflection
of the overall well-being of the United Kingdom (Hyndman, 2018). Considering the significant role these organiza-
tions play in supporting communities, reducing poverty, and serving vulnerable groups, there are strong economic and
social development reasons to conduct research on this important cohort of organizations. TSOs and socially oriented
SMEs are an important part of the U.K. ecosystem, but they are particularly vulnerable to volatile demand patterns,
funding pressures, and a lack of volunteer support during times of crisis. As a result, this has implications for their
organizational viability and sustainability.3
The COVID-19 pandemic has placed the SME sector under immense pressure, arguably much more acute than that
experienced by larger businesses (Hurley et al., 2021). Given widespread economic uncertainty, job losses, decreased
demand for goods and services, and financial losses, many businesses have been forced to close temporarily or sig-
nificantly scale back activities, leading to significant financial losses. Kalemli-Ozcan et al. (2020) estimate the impact
of the pandemic on SME failures across 17 countries. The authors find an increase in failure rates of approximately
9 percentage points in the absence of government support. Accommodation & Food Services, Arts, Entertainment &
Recreation, Education, and Other Services are among the most affected. Based on survey data from 2500 U.S. SMEs,
Bloom et al. (2021) find a substantial negative impact of the COVID-19 pandemic on sales. This peaks in the second
quarter of 2020, with an average loss of 29%. Other evidence suggests that the COVID-19 has a significant detrimental
impact on commercial SMEs and entrepreneurial activities (Belitski et al., 2022; Hurley et al., 2021). However, to date
there remains a paucity of evidence regarding the impacts of the COVID-19 pandemic on TSOs and socially oriented
SMEs. Consequently, there is an urgent need to fill this evidence gap in order to understand the ability of these orga-
nizations to respond quickly and effectively to emerging challenges, and thus fulfil their mission of creating positive
change in society.
Despite a paucity of evidence, several recent studies are of note in this regard. Plaisance (2022) investigates the
resilience of French arts and cultural nonprofit organizations during the COVID-19 crisis. The authors find that the
primary financial challenge faced by nonprofit organizations is a decline in income derived from membership fees.
Moreover, 31% of nonprofit organizations lost touch with volunteers, while 24% altered their overarching mission.
Despite significant disruptions in activities (compared to other sectors), nonprofit organizations show high resilience
via an ability to respond quickly to customer needs and other changing circumstances. Kober and Thambar (2021)
apply the Barbera et al. (2020) financial resilience framework to investigate the importance of accounting in shaping
the financial resilience of charities during the COVID-19 pandemic. The authors highlight the importance of having
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LIÑARES-ZEGARRA and WILSON 5
slack resources in organizational adaptation, and how accounting practices (such as budgeting, forecasting, and
financial and nonfinancial performance reporting) are crucial for a charity’s ability to adapt and cope with the financial
challenges posed by the COVID-19 pandemic. The COVID-19 pandemic has significantly impacted the charity sector,
leading to a crisis brought about by decreased income and increased demand for services. A study by Pro Bono
Economics (2020) reveals the presence of a £10.1 billion funding gap for U.K. charities as a result of COVID-19, with
projections indicating a decrease in income by £6.7 billion and an increase in demand for their services of £3.4 billion.
Hyndman (2020) characterizes the impact of COVID-19 on the U.K. charity sector as a “perfect storm” with substantial
loss of income and increased demand for services.
In the context of social enterprises, limited research has been conducted on the role of these organizations dur-
ing pandemics and crises. Bacq and Lumpkin (2021) emphasize the importance of reassessing the role of social
entrepreneurs in light of major societal issues such as the COVID-19 pandemic, and propose that their function be
expanded from being sole agents of change to orchestrators of collective resources. Another challenge that social
enterprises may encounter during the COVID-19 pandemic is to balance their social and economic goals (Weaver,
2020). This could result in “mission drift”, whereby social enterprises prioritize economic goals over social ones (Corn-
forth, 2014), or the need for "mission agility" (Bacq & Lumpkin, 2021), where the organization can adapt its mission in
response to societal needs.
Recent research explores the impact of the COVID-19 pandemic on the survival of (CSR-oriented) socially oriented
SMEs. Magrizos et al. (2021) suggest that SMEs that are skilled in stakeholder management benefit from implementing
CSR strategies, improving their financial performance during a crisis (such as the COVID-19 pandemic). This study is
of particular significance given that it highlights the potential positive effects of CSR during crisis periods, and thus
contributes to the limited research in the area of CSR in countries undergoing economic crisis. Wellalage et al. (2022)
use data from 6597 firms in 13 developing countries to investigate the relationship between environmental efficiency
and financing for SMEs during the pandemic. The results suggest that being environmentally conscious can enhance
trust and financial stability during times of crisis. Environmentally responsible firms enjoyed better access to external
(bank, nonbank, and trade credit) finance during the COVID-19 pandemic.
The impact of COVID-19 on TSOs and socially oriented SMEs is a complex topic that requires further research,
taking account of the varied contexts and business models of these organizations. Our research aims to contribute to
the literature in this area and provide valuable insights into the resilience of these organizations during the pandemic,
informing future policy decisions on supporting them during crises. Despite the challenges brought about by the pan-
demic, these organizations have demonstrated adaptability and a continued positive contribution to society. However,
to date empirical research on the resilience of these organizations during the COVID-19 pandemic (or other stressed
periods) is somewhat limited.
3 DATA AND METHODOLOGY
3.1 Mapping the socially responsible SME ecosystem
The LSBS is a large-scale telephone survey of owners, proprietors, managing directors, and senior directors of SMEs
based in the United Kingdom. The survey combines cross-sectional and longitudinal data and categorizes SMEs into
four types: traditional nonprofit, social enterprises, socially oriented, and commercial SMEs. For the purposes of this
study, we often refer to social enterprises and traditional nonprofits collectively as TSOs. However, in our empirical
analysis, we will analyze social enterprises and traditional nonprofits separately. In doing so, we gain a deeper under-
standing of the unique characteristics and challenges of each organizational type and thus provide tailored insights
and recommendations pertaining to social enterprises and traditional nonprofits. Moreover, this approach allows us
to draw systematic comparisons across different types of organizations with varied levels of social objectives, and
differential resilience to unexpected external shocks.
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6 LIÑARES-ZEGARRA and WILSON
F I G U R E 1 Decision tree to identify socially responsible SMEs. Source: Longitudinal Small Business Survey Year 3
(2017): Technical Report. Note: “For-profit” legal forms include sole proprietorship/trader, private limited company
(by shares), public limited company, private unlimited company, and foreign company. “Other” legal forms include
partnerships, limited liability partnerships, private company (limited by guarantee), co-operative, “other,” do not
know, and refused answers. “Social” legal forms include community interest company (limited by guarantee or
shares), friendly society, industrial and provident society, trust, unincorporated association, community benefit
society, and charitable un/incorporated organization. Env., environmental; S/E, social or environmental. For the
purposes of this study, we will refer to social enterprises and traditional nonprofits as third-sector organizations
(TSOs). However, we will analyze them separately. By analyzing each type separately, we can gain a deeper
understanding of the unique characteristics and challenges of each organization and provide tailored insights and
recommendations for each group.
Social enterprises include organizations that have identifiable social/environmental goals; generate income chiefly
from trading activities (i.e., engage in entrepreneurial activity); and use surplus/profit to further social/environmental
goals. Social enterprises also include organizations that pursue social goals and generate more than 50% of income
from trading activities. Traditional nonprofits are organizations that pursue social goals but generate less than 50% of
income from trading activities.4 Socially oriented SMEs are enterprises that have social/environmental goals and gen-
erate income chiefly from trading activities, but do not use their surplus/profit to further those social/environmental
goals. Finally, Commercial SMEs have clear commercial and financial goals, and this is the key characteristic that makes
them different from socially oriented SMEs. Figure 1 provides a more detailed overview of the classification.
3.2 longitudinal Small Business Survey
The LSBS contains detailed information regarding the characteristics of our sample, ranging from basic demographic
data to various economic variables, including business social/environmental orientation. Table 1 provides a detailed
definition of all variables used in the empirical analysis.
Table 2 presents descriptive statistics of our overall sample. Seventy percent of our sample considered COVID-19
as a major obstacle for their business. Regarding the impact of lockdowns on businesses, figures suggest that 78%
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LIÑARES-ZEGARRA and WILSON 7
TA B L E 1 Variable definition.
Variable Definition LSBS code
Major obstacle Which of the following would you say are major G2K
obstacles to the success of your business in
general?
COVID-19 COVID-19 as a major obstacle for the business
Business impact during Which of the following statements best describes GC1
lockdown how your [ANSWER AT A-2] adapted during the
lockdown restrictions from the end of March to
the middle of June 2020?
Closed down or reduced Closed down completely (temporarily) or operations
operations were reduced
Unaffected Unaffected by COVID-19 restrictions
Increased operations Operations were increased
Future plans affected by Have plans [over the next 3 years] been affected by R4A(A-J)
COVID-19 the coronavirus COVID-19 pandemic? If Yes:
Which plans?
Increase the skills of the workforce (*), increase the
leadership capability of managers (*), capital
investment (in premises, machinery, etc.) in the
United Kingdom, capital investment (in premises,
machinery, etc.) in overseas markets, develop and
launch new products/services, introduce new
working practices, invest in R&D, increase export
sales or begin selling to new overseas markets,
recruitment of new staff in the United Kingdom
(*), recruitment of new staff in overseas offices (*).
Note: (*) Only asked to business with employees.
Use of COVID funding Has your company used any . . . ? H100A_1 and H100A_2
COVID-funding (loans) Coronavirus COVID-19 government-backed
accredited loans or finance agreements such as
Coronavirus Business Interruption Loan and
Bounce Back Loan
COVID-funding (grants) Coronavirus COVID-19 business grants funded by
government or local authority
SME classification SOCENT
Commercial SMEs (base See Figure 1. For the purposes of this study, we will
category) refer to social enterprises and traditional
Traditional nonprofit nonprofits as third-sector organizations (TSOs).
Social enterprise However, we will analyze them separately. By
Socially orientated SME analyzing each type separately, we can gain a
deeper understanding of the unique
characteristics and challenges of each
organization and provide tailored insights and
recommendations for each group.
Aims to grow Aim to grow sales over the next 3 years. R1
Size A2SPSS1
Zero employees (base Zero employee business had no employees on their
category) payroll (excluding owners and partners) at the
time of the interview.
(Continues)
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8 LIÑARES-ZEGARRA and WILSON
TA B L E 1 (Continued)
Variable Definition LSBS code
Micro 1–9 employees.
Small 10–49 employees.
Medium 50–249 employees.
Business age Age of the firm. A6SUM and A6, missing
values for 2016 are
completed with values
from 2015
0–5 years (base
category)
6–10 years
11–20 years
20+ years
Turnover change Turnover in the past 12 months, compared with the P2
previous 12 months.
Decreased (base
category)
Stayed roughly the same
Increased
Profit Firm generates a profit or surplus after considering P12
all sources of income in the last financial year.
Urban area Broad urban/rural categorization from postcode. URBRUR2
Female led Business is women led. WLED
Minority ethnic led Business is MEG led. MLED
Family owned Business is a family-owned business (i.e., one that is A12
majority owned by members of the same family).
Business plan The business has a formal written business plan. F5
Region Region where the firm has its headquarters. NATION
England (base category)
Scotland
Wales
Northern Ireland
Broad sector Industry sector SECTOR
Manufacturing sector Production and construction (SIC 2007: ABCDEF).
(base category)
Transportation and Transport, retail, and food service/accommodation
retail services (SIC 2007: GHI).
Business services Business services (SIC 2007: JKLMN).
Other services Other services (SIC 2007: PQRS).
Note: This Table shows variable names and definitions of our dependent and explanatory variables. All variables were gathered
from the Longitudinal Small Business Survey, 2019–2020.
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LIÑARES-ZEGARRA and WILSON 9
TA B L E 2 Summary statistics.
Mean SD N
MAJOR OBSTACLE
COVID-19 0.704 0.457 2516
BUSINESS IMPACT DURING LOCKDOWN
Closed down reduced operations 0.785 0.411 7596
Unaffected 0.156 0.362 7596
Increased operations 0.060 0.237 7596
SMEs WITH PLANS AFFECTED BY COVID-19
A: Increase the skills of the workforce 0.424 0.905 1270
B: Increase the leadership capability of managers 0.389 0.966 874
C: Capital investment (in premises, machinery, etc.) in the United 0.507 0.614 764
Kingdom
D: Capital investment (in premises, machinery, etc.) in overseas 0.613 0.589 125
markets
E: Develop and launch new products/services 0.552 0.521 892
F: Introduce new working practices 0.477 0.579 1024
G: Invest in R&D 0.428 0.639 629
H: Increase export sales or begin selling to new overseas markets 0.540 0.565 388
I: Recruitment of new staff in the United Kingdom 0.498 0.688 1279
J: Recruitment of new staff in overseas offices 0.498 0.660 116
USE OF COVID FUNDING
COVID-funding (loans) 0.276 0.615 7462
COVID-funding (grants) 0.312 0.629 7445
TYPES OF ORGANIZATIONS AND BUSINESSES
Commercial SME (base category) 0.680 0.467 10,883
Traditional nonprofit 0.042 0.201 10,883
Social enterprise 0.082 0.274 10,883
Socially orientated SME 0.196 0.397 10,883
CONTROL VARIABLES
Entrepreneur orientation
Aims to grow 0.585 0.493 18,621
Size
Zero employees (base category) 0.759 0.427 18,621
Micro (1–9) 0.198 0.399 18,621
Small (10–49) 0.036 0.187 18,621
Medium (50–249) 0.006 0.077 18,621
Business age
0–5 years (base category) 0.136 0.343 18,559
6–10 years 0.184 0.387 18,559
11–20 years 0.298 0.458 18,559
20+ years 0.382 0.486 18,559
(Continues)
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10 LIÑARES-ZEGARRA and WILSON
TA B L E 2 (Continued)
Mean SD N
Turnover change
Decreased (base category) 0.383 0.486 17,897
Stayed the same 0.392 0.488 17,897
Increased 0.226 0.418 17,897
Profitability
Profit 0.764 0.425 17,648
Business characteristics
Urban area 0.686 0.464 18,100
Family owned 0.858 0.349 18,573
Business plan 0.283 0.451 17,966
Female led 0.186 0.389 17,757
Minority ethnic led 0.045 0.208 17,258
Region
England (base category) 0.884 0.320 18,621
Scotland 0.059 0.236 18,621
Wales 0.035 0.183 18,621
Northern Ireland 0.022 0.148 18,621
Broad sector
Manufacturing sector (base category) 0.248 0.432 18,621
Transportation and retail services 0.188 0.391 18,621
Business services 0.337 0.473 18,621
Other services 0.227 0.419 18,621
Note: This table reports the summary statistics using data from the Longitudinal Small Business Survey, 2019–2020. Cross
sectional survey weights applied to represent the population of SMEs in the United Kingdom. Respondents who answer “I do
not know” or refused to answer are not included in the analyses. Variable definitions are reported in Table 1.
of our sample had to either close or reduce their operations. Only 15% of our sample were not affected, and a mere
6% experienced an increase in their operations. As for the effect of COVID-19 on future plans, 42% of our sample
had their plans to increase the skills of their workforce affected, while 38.9% saw plans to enhance their leadership
capabilities impacted. Half of the sample reported that their capital investments in the United Kingdom were affected,
while 61% said that their investments in overseas markets were impacted. In addition, more than half of sample experi-
enced challenges in their plans to develop and launch new products or services, while nearly half saw plans affected to
introduce new working practices and recruiting U.K. or international staff. Plans to invest in research and development
and to increase export sales or begin selling abroad were also affected for 42% and 54% of the sample, respectively.
Twenty-seven percent of the sample had access to COVID-19 government-backed accredited loans or finance agree-
ments, such as the Coronavirus Business Interruption Loan and Bounce Back Loan, for funding related to the pandemic.
Additionally, 31% of the sample were able to access COVID-19 business grants funded by the government or local
authorities.
Commercial SMEs represent 67% of our sample, followed by socially oriented SMEs (19.6%) and TSOs (which
comprise social enterprises [8.1%] and traditional nonprofits [4.2%]). Fifty-eight percent of the organizations and busi-
nesses in our sample are growth oriented, and therefore aim to grow sales over the next 3 years. Firm size is measured
by the number of employees reported by the company to be currently on the payroll, excluding owners and partners,
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LIÑARES-ZEGARRA and WILSON 11
across all sites of the firm. The majority of our sample belong to the category of zero employees (75.9%) followed
by micro-sized (19.8%), small-sized (3.6%), and medium-sized (0.6%) business. To control for the age, a set of binary
variables covering from start-ups (0–5 years) to mature business (20+ years) are included. The distribution across
age categories is relatively homogenous, although the majority of our sample are classified in the 20+ years category
(37.7%).
The base category of “Decreased” in turnover change indicates that 38.27% of our sample experienced a decrease
in turnover, while 39.17% maintained the same level, and 22.56% observed an increase. The majority of our sample,
76.37%, were profitable, while 68.60% were located in urban areas and 85.82% were family owned. A minority of
our sample, 18.55% and 4.52%, were female led and minority ethnic led, respectively. The majority of our sample,
88.38%, were based in England, while 5.91%, 3.47%, and 2.24% were located in Scotland, Wales, and Northern Ireland,
respectively. In terms of broad sector, the largest proportion of observations in our sample, 33.68%, belonged to busi-
ness services, followed by other services (22.68%), transportation and retail services (18.81%), and the manufacturing
sector (24.84%).
Table 3 shows the correlations between the explanatory variables to assess multicollinearity. The highest corre-
lation is 0.33 (between size and business plan dummy). Hence, multicollinearity does not appear to present a critical
concern for our analysis.
3.3 Methodology
The present study utilizes the two most recent waves (2019–2020) of the LSBS. The LSBS survey allows us to exploit
the longitudinal nature of the data collected, and thus deal with endogeneity concerns by using lagged variables. We
rely on two empirical approaches (comprising multinomial probit (MNP) and probit models) in order to investigate how
the pandemic has affected TSOs and socially oriented SMEs in terms of operations and business plans.
3.3.1 Multinomial Probit (MNP) model
We use an MNP regression to investigate how TSOs and socially oriented SMEs have adapted their business opera-
tions during the lockdown restrictions imposed by the U.K. government following the onset and spread of the COVID
19 pandemic. The MNP model is used with discrete dependent variables that take on more than two outcomes that do
not have a natural ordering (Cameron & Trivedi, 2005).
j, ij The utility of alternative j how your businessTSOs and socially oriented SMEs i adapted its their business operations
during the lockdown restrictions closed downreduced operations unaffected increased operations is a function of firm-level
characteristics and a stochastic error and therefore modeled as
Uij = xij′ 𝛽 + 𝜀ij , (1)
where xij′ is a vector of covariates and the errors are assumed to be normally distributed, with ε ∼ N(0, Σ), where ε = (εi1 ,
εi2 , εi3 ). The probability that alternative j is observed is
{ ( )′ }
pij = Pr (yi = j) = Pr 𝜀ik − 𝜀ij ≤ xij − xik 𝛽 , forall k, (2)
where yi is a random variable that indicates how SMEs TSOs and socially oriented SMEs have adapted their business
operations during the lockdown restrictions. The MNP model is an extension of the binary probit model that allows
the coefficients of the explanatory variables to vary across alternatives and allow us to assess whether specific
characteristics are associated with higher probabilities of observing alternative j.
12
TA B L E 3 Correlation matrix.
Variables (1) (2) (3) (4) (5) (6) (7) (8) (9) (10) (11) (12) (13)
(1) Social enterprise 1.000
(2) Aim to grow 0.027* 1.000
(3) Firm size 0.051* 0.246* 1.000
(4) Firm age −0.020 −0.091* 0.179* 1.000
(5) Turnover change 0.017 0.075* 0.131* −0.055* 1.000
(6) Profit −0.067* −0.002 0.056* 0.020* 0.250* 1.000
(7) Urban −0.016 0.058* 0.079* −0.027* −0.014 −0.026* 1.000
(8) Family business −0.067* −0.060* −0.256* −0.078* −0.073* 0.055* −0.130* 1.000
(9) Business plan 0.112* 0.177* 0.328* –0.013 0.070* –0.034* 0.053* –0.206* 1.000
(10) Women led 0.035* –0.019 –0.028* –0.046* –0.040* –0.051* –0.002 0.059* 0.004 1.000
(11) MEG led 0.041* 0.032* 0.013 −0.078* −0.007 −0.032* 0.096* 0.002 0.030* –0.002 1.000
(12) Region 0.027* 0.003 0.016 −0.027* 0.011 0.022* −0.099* 0.020* 0.003 −0.004 −0.055* 1.000
(13) Broad Sector 0.092* –0.060* –0.063* –0.040* –0.019 –0.079* 0.133* −0.185* 0.084* 0.172* 0.043* −0.067* 1.000
Note: This table reports the correlation matrix between all variables used in this study.
*Statistical significance at the 1% level.
LIÑARES-ZEGARRA and WILSON
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LIÑARES-ZEGARRA and WILSON 13
All results associated with these models are presented in terms of average marginal effects (AMEs), given that we
are interested in the change in the probability associated to changes in business characteristics and/or specific orga-
nizational forms. Standard errors are clustered at regional level to allow for individual correlations within the same
geographic area.
3.3.2 Probit model
Probit models are used to investigate the impact of COVID-19 on business operations (e.g., major obstacle to business
success), future plans and acess to COVID–19 funding. In this case, the dependent variable is equal to 1 if the SME i
exhibit a specific consequence derived from the COVID-19 pandemic, and 0 otherwise.
Pr (Obstacles∕Future Plans∕COVID − −19Fundingi = 1) = Φ (Xi 𝛽 + vi ) , (3)
where νi are independent and identically distributed (i.i.d., N(0, 𝜎𝜈2 ), and Φ is the standard normal cumulative distri-
bution function. We include a wide range of independent variables, which are expected to affect their perceptions
about COVID-19, future business plans and access to COVID–19 funding. These include organizational form, size, age,
various firm-level characteristics, and industry and regional fixed effects. In addition, our empirical approach (where
appropriate) uses lagged independent variables for growth ambition, changes in turnover, and profitability to mitigate
endogeneity concerns arising from reverse causality. All results associated with these models are presented in terms
of AMEs and errors are clustered at the regional level to allow for individual correlations within the same geographical
area.
4 RESULTS AND DISCUSSION
In this section, we present the main results. Specifically, our results cover four key areas with a special focus on TSOs
(nonprofit organizations and social enterprises) and socially oriented SMEs: (1) the extent to which these organiza-
tions and businesses view COVID-19 as a major obstacle to their success; (2) the impact of lockdown restrictions on
business operations between March and June 2020; (3) the effect of the pandemic on future business plans across
10 key categories; and (4) the use and access of COVID-19 funding from the U.K. government, including grants and
loans. By examining these areas, we aim to provide a comprehensive understanding of the challenges faced by TSOs
and socially oriented SMEs during the COVID-19 pandemic and their responses therein.
4.1 COVID-19 as a major obstacle to business success
Table 4 presents results on whether TSOs and socially oriented SMEs perceived the COVID-19 pandemic as a major
obstacle to their business success. Model 1 includes a comprehensive range of business characteristics as control vari-
ables, while Model 2 incorporates two more variables that relate to the firm’s management: whether the business
is led by women and/or whether it is MEG led. Results suggest that social enterprises are less likely to consider the
pandemic as a major obstacle for their business compared to commercial SMES. This is in contrast to socially focused
SMEs, which were approximately 3.8% more likely than commercial SMEs to view the COVID-19 pandemic as a signif-
icant obstacle to their business. Overall, this result suggests that social enterprises may be more resilient in the face of
unexpected challenges like the COVID-19 pandemic as they can be more flexible and adaptive in their business mod-
els, while socially focused SMEs may face greater challenges in maintaining their social and environmental objectives
in times of crisis as they have specific commercial targets to achieve.
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14 LIÑARES-ZEGARRA and WILSON
TA B L E 4 COVID-19 pandemic as major obstacles to the success of your business in general.
Model 1 Model 2
Traditional nonprofitt – 1 0.035 0.063
(0.75) (1.33)
Social enterpriset – 1 –0.025*** –0.033***
(−11.41) (−10.39)
Socially oriented SMEt – 1 0.042*** 0.038***
(3.17) (4.34)
Aims to growt – 1 –0.044* –0.049**
(−1.80) (−2.28)
Size: Micro 0.026*** 0.021***
(3.65) (3.03)
Size: Small 0.010 0.022
(0.48) (1.36)
Size: Medium –0.013 –0.007
(−0.33) (−0.13)
Business age: 6–10 years –0.179*** –0.185***
(−3.09) (−3.71)
Business age: 11–20 years 0.013 0.016***
(1.18) (5.18)
Business age: 20+ years –0.129** –0.117**
(−2.34) (−2.52)
Turnover change (stayed the same)t – 1 –0.008 –0.031***
(−0.68) (−4.47)
Turnover change (increased)t – 1 –0.048*** –0.071***
(−3.23) (−4.44)
Profitt – 1 0.000 0.016
(0.02) (0.97)
Locationt : Urban area 0.067*** 0.075***
(3.29) (3.66)
Family owned 0.018 0.032
(0.45) (0.70)
Business plan 0.051 0.052
(1.63) (1.61)
Female ledt – 1 0.066
(1.19)
Minority ethnic ledt – 1 0.007
(0.33)
Fixed effects
Regional FEs Yes Yes
(Continues)
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LIÑARES-ZEGARRA and WILSON 15
TA B L E 4 (Continued)
Model 1 Model 2
Industry FEs Yes Yes
Observations 1424 1317
Log pseudo-likelihood −836.401 −798.935
R2 0.067 0.067
AIC 1676.801 1601.869
BIC 1687.324 1612.236
Note: This table shows average marginal effects (AMEs) from a probit model of SMEs’ characteristics on the probability of
considering COVID-19 as a major obstacle for their businesses. All regressions include a constant term. The base categories
for categorical variables are as follows: zero employees (size), 0–5 years (business age), 18−30 years old (owner’s age), and
decreased (turnover change). Commercial SME is the base category for comparing organizational forms. For the purposes of
this study, we will refer to social enterprises and traditional nonprofits as third-sector organizations (TSOs). However, for the
purposes of our empirical analysis, we will analyze them separately. All models include industry and regional fixed effects.
Survey weights applied to represent the population of SMEs in the United Kingdom. Z-statistics adjusted for clustering at
regional level are reported in parentheses. Statistical significance at the 10%, 5%, and 1% levels are showed by *, ** and ***,
respectively.
4.2 Impact of government-imposed lockdown restrictions on business operations
Table 5 provides additional information regarding how TSOs and socially oriented SMEs adapted their operations dur-
ing the lockdown restrictions compared to commercial SMEs. The results suggest that traditional nonprofits, social
enterprises, and socially oriented SMEs were more likely to see their operations increased as a results of lockdown
restrictions from the end of March to the middle of June 2020 compared to commercial SMEs. This suggests that
these organizations have shown some level of resilience during the economic downturn and has been able to adapt
to the changing market conditions caused by the pandemic by pivoting their operations to meet the evolving needs of
their local communities.
4.3 Impact of the COVID-19 pandemic on future business plans
The LSBS allows us to analyze how plans to pursue specific activities over the next 3 years have been affected by the
COVID-19 pandemic (see Tables 6 and 7). Table 6 presents results on the impact of the COVID-19 pandemic on future
plans for TSOs and socially oriented SMEs compared to commercial SMEs. Results suggest that traditional nonprofit
organizations and socially oriented SMEs were 23% and 3% less likely, respectively, to have their workforce develop-
ment plans impacted by the pandemic compared to commercial SMEs. Social enterprises and socially oriented SMEs
were 14.3% and 10.7% less likely, respectively, to have their plans to increase the leadership capability of managers
impacted by the pandemic compared to commercial SMEs. Social enterprises and socially oriented SMEs were 31.4%
and 7% less likely, respectively, to have their plans for capital investment (in premises, machinery, etc.) in the United
Kingdom impacted by the pandemic compared to commercial SMEs. However, social enterprises and socially oriented
SMEs were 58.6% and 54.7% more likely, respectively, to have their plans for capital investment (in premises, machin-
ery, etc.) in overseas markets impacted by the pandemic compared to commercial SMEs. Compared to commercial
SMEs, traditional nonprofits were 9.6% more likely to have their plans to develop and launch new products or services
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16 LIÑARES-ZEGARRA and WILSON
TA B L E 5 Which of the following statements best describes how your business adapted during the lockdown
restrictions from the end of March to the middle of June 2020?
Your business closed down Unaffected by Operations
completely (temporarily) or COVID-19 were
Operations were reduced restrictions increased
Traditional nonprofitt – 1 0.029 –0.099*** 0.070***
(0.71) (−3.89) (4.52)
Social enterpriset – 1 –0.021 –0.016 0.037***
(−1.15) (−0.69) (5.60)
Socially oriented SMEt – 1 –0.016 –0.004 0.020***
(−0.96) (−0.25) (18.50)
Aims to growt – 1 0.044*** –0.057*** 0.013**
(2.84) (−5.22) (2.48)
Size: Micro –0.006 –0.009 0.015***
(−0.58) (−0.73) (2.66)
Size: Small –0.008 –0.021 0.030***
(−0.51) (−1.31) (7.99)
Size: Medium –0.040* –0.007 0.047***
(−1.92) (−0.42) (12.90)
Business age: 6–10 years –0.012 0.026** –0.014***
(−0.86) (2.34) (−4.29)
Business age: 11–20 years 0.007 0.014 –0.021***
(0.40) (1.05) (−4.79)
Business age: 20+ years –0.037* 0.065*** –0.028***
(−1.94) (4.32) (−6.83)
Turnover change (stayed the same)t – 1 –0.086*** 0.088*** –0.002
(−3.55) (4.18) (−0.39)
Turnover change (increased)t – 1 –0.107*** 0.096*** 0.011*
(−83.31) (19.69) (1.90)
Profitt – 1 0.044*** –0.044*** 0.001
(5.92) (−6.89) (0.51)
Locationt : Urban area 0.031*** –0.040*** 0.009**
(4.50) (−9.69) (2.02)
Family owned 0.014 –0.055*** 0.041***
(1.22) (−3.60) (8.12)
Business plan 0.007 –0.024*** 0.018***
(1.25) (−2.92) (3.84)
Female ledt – 1 0.101*** –0.096*** –0.005
(64.77) (−14.33) (−0.98)
Minority ethnic ledt – 1 –0.088*** 0.041** 0.047***
(−3.39) (1.96) (9.36)
(Continues)
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LIÑARES-ZEGARRA and WILSON 17
TA B L E 5 (Continued)
Your business closed down Unaffected by Operations
completely (temporarily) or COVID-19 were
Operations were reduced restrictions increased
Fixed effects
Regional FEs Yes
Industry FEs Yes
Observations 3892
Log likelihood –2443.91
AIC 4891.835
BIC 4904.368
Note: This table shows average marginal effects from multinomial probit regressions predicting business adaptations during
lockdown. All regressions include a constant term. The base categories for categorical variables are as follows: zero employees
(size), 0–5 years (business age), 18−30 years old (owner’s age), and decreased (turnover change). Commercial SME is the base
category for comparing organizational forms. For the purposes of this study, we will refer to social enterprises and traditional
nonprofits as third-sector organizations (TSOs). However, for the purposes of our empirical analysis, we will analyze them
separately. All models include industry and regional fixed effects. Survey weights applied to represent the population of SMEs
in the United Kingdom. Z-statistics adjusted for clustering at regional level are reported in parentheses. Statistical significance
at the 10%, 5%, and 1% levels are showed by *, **, and ***, respectively.
impacted by the pandemic. On the other hand, social enterprises were 3.3% less likely than commercial SMEs to have
their plans affected by the pandemic in terms of developing and launching new products or services.
Results in Table 7 suggest that, compared to commercial SMEs, social enterprises were 18.6% less likely to have
their plans to introduce new working practices impacted by the pandemic. Compared to commercial SMEs, traditional
nonprofits were 6.8% less likely to have their plans to invest in R&D impacted by the pandemic. On the other hand,
social enterprises and socially oriented SMEs were 18.6% and 17.4%, respectively, more likely than commercial SMEs
to have their plans affected by the pandemic in terms of plans to invest in R&D. Social enterprises and socially oriented
SMEs were 53.5% and 4.7% more likely, respectively, to have their plans to increase export sales or begin selling to new
overseas markets impacted by the pandemic compared to commercial SMEs. Social enterprises and socially oriented
SMEs were 4.2% and 10.6% more likely, respectively, to have their plans of recruitment of new staff in the United
Kingdom impacted by the pandemic compared to commercial SMEs. Compared to commercial SMEs, social enterprises
were 12.1% less likely to have their plans of recruitment of new staff in overseas offices impacted by the pandemic. On
the other hand, socially oriented SMEs were 24.2% more likely than commercial SMEs to have their plans affected by
the pandemic in terms of recruitment of new staff in overseas offices.
Results in Tables 6 and 7 show that the COVID-19 pandemic has had a differential impact on the future plans of
TSOs and socially oriented SMEs compared to commercial SMEs. While traditional nonprofits and socially oriented
SMEs were less likely to have their workforce development and leadership plans impacted by the pandemic, they were
more likely to face challenges in capital investment and recruitment of new staff in overseas markets. Social enter-
prises, on the other hand, were less likely to have their plans for introducing new working practices and developing
and launching new products or services impacted by the pandemic, but more likely to face challenges in R&D invest-
ment and increasing export sales or selling to new overseas markets. These findings highlight the need for targeted
support and interventions to ensure that TSOs and socially oriented SMEs can overcome the challenges posed by the
pandemic and continue to contribute to social and economic development.
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18 LIÑARES-ZEGARRA and WILSON
TA B L E 6 SMEs with future plans over the next 3 years affected by COVID-19 pandemic.
Increase the Capital Develop and
Increase the leadership Capital investment in launch new
skills of the capability of investment in overseas prod-
workforce managers United Kingdom market ucts/services
Traditional −0.237*** −0.122 0.043 n.e 0.096**
nonprofitt – 1
(−7.90) (−1.43) (0.68) (.) (2.30)
Social 0.020 −0.143*** −0.314*** 0.586*** −0.033*
enterpriset – 1
(0.87) (−4.95) (−30.00) (907.06) (−1.80)
Socially −0.030* −0.107** −0.070* 0.547*** 0.051
oriented
SMEt – 1
(−1.92) (−1.99) (−1.70) (21.76) (0.99)
Aims to −0.064*** 0.042** 0.140*** −0.147*** 0.106**
growt – 1
(−4.84) (1.97) (31.50) (−16.21) (2.19)
Size: Micro n.e n.e 0.081*** 0.017 –0.112***
(.) (.) (2.59) (0.90) (−5.48)
Size: Small 0.014 0.002 0.151*** −0.097*** −0.058*
(0.62) (0.06) (2.66) (−30.95) (−1.69)
Size: Medium 0.106*** 0.127*** 0.143*** 0.064** −0.101
(4.09) (3.99) (3.58) (2.22) (−1.30)
Business age: −0.128*** 0.100*** −0.114** 0.531*** −0.267
6–10 years
(−27.26) (4.54) (−2.51) (49.75) (−1.45)
Business age: −0.108*** −0.089 −0.102*** 0.575*** −0.082
11–20
years
(−3.43) (−1.15) (−10.74) (14.44) (−0.62)
Business age: −0.068*** 0.066 −0.269*** −0.124*** −0.220**
20+ years
(−7.46) (1.58) (−5.61) (−6.81) (−2.48)
Turnover –0.076* –0.090*** –0.031*** –0.387*** –0.042
change
(stayed the
same)t – 1
(−1.66) (−2.95) (−3.90) (−19.01) (−1.26)
Turnover –0.085*** –0.098*** 0.033 –0.278*** –0.033***
change
(increased)t – 1
(−3.92) (−6.48) (1.15) (−68.73) (−4.06)
Profitt – 1 –0.147*** –0.103** –0.060 –0.060*** 0.205***
(−18.86) (−2.14) (−0.79) (−3.64) (6.02)
Locationt : 0.002 –0.015 0.012 –0.230*** 0.006
Urban area
(0.07) (−0.55) (0.18) (−3.11) (0.24)
(Continues)
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LIÑARES-ZEGARRA and WILSON 19
TA B L E 6 (Continued)
Increase the Increase the Capital Capital Develop and
skills of the leadership investment in investment in launch new
workforce capability of United Kingdom overseas prod-
managers market ucts/services
Family 0.090*** –0.028 0.077*** –0.431*** 0.017
owned
(8.51) (−1.17) (4.24) (−20.06) (0.72)
Business plan 0.052*** –0.074*** –0.002 –0.090*** 0.137***
(141.83) (−4.94) (−0.21) (−5.36) (41.32)
Female 0.037* 0.047 0.121 0.282*** 0.115***
ledt – 1
(1.92) (0.53) (0.95) (3.54) (5.78)
Minority 0.141*** 0.151*** 0.005 –0.863*** –0.050**
ethnic
ledt – 1
(15.26) (22.90) (0.28) (−32.28) (−2.36)
Fixed effects
Regional FEs Yes Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes Yes
Observations 672 451 399 58 456
Log pseudo- −142.109 −77.933 −148.533 −8.296 −283.218
likelihood
R2 0.043 0.065 0.166 0.666 0.072
AIC 288.219 159.866 301.067 18.591 570.435
BIC 297.239 168.089 309.045 20.652 578.680
Note: This table shows average marginal effects from probit regressions. All regressions include a constant term. The base cat-
egories for categorical variables are as follows: zero employees (size), 0–5 years (business age), 18−30 years old (owner’s age),
and decreased (turnover change). Commercial SME is the base category for comparing organizational forms. For the purposes
of this study, we will refer to social enterprises and traditional nonprofits as third-sector organizations (TSOs). However, for
the purposes of our empirical analysis, we will analyze them separately. All models include industry and regional fixed effects.
Survey weights applied to represent the population of SMEs in the United Kingdom. Sample is restricted to SMEs with future
plans (stated in columns in the Table below) over the next 3 years. Z-statistics adjusted for clustering at regional level are
reported in parentheses. The term “n.e” stands for “not estimable.” This condition arises when the outcome variable can be
perfectly predicted from a subset of the data, or when there are insufficient observations to compute the marginal effects.
Statistical significance at the 10%, 5%, and 1% levels are showed by *, **, and ***, respectively.
4.4 Access and use of COVID-19 funding
Finally, Table 8 presents results on the use of COVID-19 government-backed accredited loans or grants funded by
government or local authority by TSOs and socially oriented SMEs. Models 1 and 3 include a comprehensive range
of business characteristics as control variables, while Models 2 and 4 incorporate two more variables that relate to
the firm’s management: whether the business is led by women and/or whether it is MEG led. Results suggest that,
compared to commercial SMEs, both traditional nonprofit and social enterprises were 18.8% and 4.4%, respectively,
less likely to use COVID-19 government-backed accredited loans or finance agreements such as Business Interrup-
tion and Bounce Back Loans. This in turn suggests that these organizations may need alternative forms of support
or financing to weather economic disruptions such as the COVID-19 pandemic. This could include targeted grants or
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20 LIÑARES-ZEGARRA and WILSON
TA B L E 7 SMEs with future plans over the next 3 years affected by COVID-19 pandemic.
Increase export
sales or begin
Introduce new selling to new Recruitment of Recruitment of
working Invest in overseas new staff in the new staff in
practices R&D markets United Kingdom overseas offices
Traditional −0.135 −0.068*** n.e 0.038 −0.013
nonprofitt – 1
(−0.87) (−4.12) (.) (0.81) (−0.18)
Social –0.186*** 0.186*** 0.535*** 0.042*** –0.121**
enterpriset – 1
(−6.28) (6.60) (12.95) (4.09) (−2.46)
Socially 0.089 0.174*** 0.047* 0.106*** 0.242**
oriented
SMEt – 1
(1.48) (5.78) (1.70) (9.26) (2.43)
Aims to 0.102** 0.032 0.003 0.066*** –0.060
growt – 1
(2.55) (0.92) (0.04) (5.36) (−0.56)
Size: Micro –0.035 –0.040 0.070* –0.109*** 0.467***
(−1.03) (−0.88) (1.87) (−6.86) (5.82)
Size: Small –0.058 –0.032 0.078 –0.148*** 0.236***
(−0.51) (−0.85) (0.99) (−7.38) (2.99)
Size: Medium 0.074 –0.006 –0.018 –0.077*** 0.336
(0.64) (−0.09) (−0.26) (−2.89) (1.62)
Business age: 0.110 –0.453*** –0.239*** –0.147*** 0.012
6–10 years
(0.97) (−12.41) (−25.06) (−9.22) (0.20)
Business age: 0.092** –0.270*** 0.026 –0.091*** 0.143***
11–20
years
(2.46) (−35.09) (1.56) (−14.94) (3.22)
Business age: 0.048*** –0.259*** –0.170*** –0.138*** –0.010
20+ years
(3.07) (−5.96) (−3.23) (−28.14) (−0.08)
Turnover 0.003 –0.111*** –0.217*** –0.110*** –0.293***
change
(stayed the
same)t – 1
(0.06) (−36.09) (−145.77) (−9.91) (−39.07)
Turnover –0.078* –0.193*** –0.232*** –0.167*** –0.206***
change
(increased)t – 1
(−1.80) (−10.50) (−7.62) (−33.18) (−27.35)
Profitt – 1 –0.090** 0.085*** –0.005 –0.039*** 0.190***
(−1.98) (2.59) (−0.20) (−4.73) (7.88)
(Continues)
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LIÑARES-ZEGARRA and WILSON 21
TA B L E 7 (Continued)
Introduce new Invest in Increase export Recruitment of Recruitment of
working R&D sales or begin new staff in the new staff in
practices selling to new United Kingdom overseas offices
overseas
markets
Locationt : 0.137*** 0.045 0.052*** –0.052*** 0.139*
Urban area
(3.10) (1.21) (49.96) (−8.16) (1.85)
Family 0.030*** 0.085*** –0.028** 0.072*** –0.062**
owned
(3.39) (5.16) (−1.98) (8.33) (−2.22)
Business plan 0.020 0.129*** –0.090*** 0.138*** –0.016***
(0.70) (12.28) (−6.19) (17.39) (−2.84)
Female 0.034 –0.170*** –0.053*** –0.066*** 0.131***
ledt – 1
(0.23) (−15.32) (−8.56) (−3.94) (2.70)
Minority –0.130** 0.043*** –0.182*** –0.155*** 0.014**
ethnic
ledt – 1
(−2.51) (3.22) (−15.97) (−23.41) (2.30)
Fixed effects
Regional FEs Yes Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes Yes
Observations 511 333 207 658 66
Log pseudo- –243.464 –108.114 –91.832 –220.690 –7.739
likelihood
R2 0.153 0.228 0.205 0.072 0.649
AIC 490.928 220.228 187.665 445.381 17.479
BIC 499.401 227.844 194.330 454.359 19.668
Note: This table shows average marginal effects from probit regressions. All regressions include a constant term. The base cat-
egories for categorical variables are as follows: zero employees (size), 0–5 years (business age), 18−30 years old (owner’s age),
and decreased (turnover change). Commercial SME is the base category for comparing organizational forms. For the purposes
of this study, we will refer to social enterprises and traditional nonprofits as third-sector organizations (TSOs). However, for
the purposes of our empirical analysis, we will analyze them separately. All models include industry and regional fixed effects.
Survey weights applied to represent the population of SMEs in the United Kingdom. Sample is restricted to SMEs with future
plans (stated in columns in the Table below) over the next 3 years. Z-statistics adjusted for clustering at regional level are
reported in parentheses. The term “n.e” stands for “not estimable.” This condition arises when the outcome variable can be
perfectly predicted from a subset of the data, or when there are insufficient observations to compute the marginal effects.
Statistical significance at the 10%, 5%, and 1% levels are showed by *, ** and ***, respectively.
subsidies, streamlined administrative processes for accessing specific type of funding, or other forms of financial assis-
tance. Results for COVID-19 business grants funded by government or local authority indicate that socially oriented
SMEs were more likely to use the grants, with a range of 0.6%–1.9%, compared to commercial SMEs. The findings
for traditional nonprofit organizations and social enterprises were less clear. The significance of the coefficients varies
across different models, but the results suggest that social enterprises were 2.1% more likely to use the grants in Model
3, while traditional nonprofits were 5.3% less likely to use the grants in Model 4, compared to SMEs.
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22 LIÑARES-ZEGARRA and WILSON
TA B L E 8 Use of coronavirus COVID-19 government-backed accredited loans, finance agreements, or grants
funded by government or local authority.
Coronavirus COVID-19
government-backed accredited loans or
finance agreements such as Coronavirus
Business Interruption Loan and Bounce Coronavirus COVID-19 business grants
Back Loan funded by government or local authority
Model 1 Model 2 Model 3 Model 4
Traditional –0.188*** –0.204*** –0.079 –0.053***
nonprofitt – 1
(−37.56) (−26.04) (−1.23) (−5.97)
Social –0.044** –0.041** 0.021** 0.026
enterpriset – 1
(−2.51) (−2.19) (2.25) (1.59)
Socially oriented –0.007 –0.006 0.019** 0.006***
SMEt – 1
(−0.77) (−0.68) (2.46) (4.56)
Aims to growt – 1 0.124*** 0.126*** 0.093*** 0.055***
(26.63) (34.62) (6.09) (3.26)
Size: Micro 0.153*** 0.151*** 0.174*** 0.209***
(4.97) (4.65) (293.09) (23.54)
Size: Small 0.181*** 0.169*** 0.117*** 0.142***
(5.20) (4.82) (7.94) (10.66)
Size: Medium 0.131*** 0.120*** –0.039*** –0.003
(5.80) (5.60) (−2.99) (−0.59)
Business age: 0.010 0.026 –0.060 0.003
6–10 years
(0.65) (1.44) (−1.61) (0.35)
Business age: –0.011 0.003 –0.032 –0.012
11–20 years
(−1.21) (0.35) (−0.78) (−0.97)
Business age: –0.075*** –0.057** –0.022 –0.027***
20+ years
(−4.00) (−2.51) (−0.43) (−6.56)
Turnover change 0.004 0.008 –0.053*** 0.015
(stayed the
same)t – 1
(0.30) (0.54) (−2.88) (0.54)
Turnover change –0.012 –0.006 –0.049 –0.006
(increased)t – 1
(−0.86) (−0.41) (−1.58) (−0.37)
Profitt – 1 –0.001 –0.010** 0.019 –0.044***
(−0.14) (−1.99) (0.61) (−8.61)
Locationt : Urban –0.003 –0.008 –0.021 0.010
area
(−0.20) (−0.48) (−1.40) (0.62)
(Continues)
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LIÑARES-ZEGARRA and WILSON 23
TA B L E 8 (Continued)
Coronavirus COVID-19 Coronavirus COVID-19 business grants
government-backed accredited loans or funded by government or local authority
finance agreements such as Coronavirus
Business Interruption Loan and Bounce
Back Loan
Family owned 0.038 0.021 –0.049*** 0.052***
(1.58) (0.69) (−4.17) (12.58)
Business plan 0.026*** 0.031*** –0.005 0.014*
(4.45) (4.49) (−0.76) (1.70)
Female ledt – 1 –0.054*** 0.025***
(−4.67) (2.74)
Minority ethnic 0.058*** 0.034***
ledt – 1
(2.79) (7.17)
Fixed effects
Regional FEs Yes Yes Yes Yes
Industry FEs Yes Yes Yes Yes
Observations 4229 3870 4227 3868
Log pseudo- –2134.441 –2018.242 –2405.890 –2332.768
likelihood
R2 0.081 0.082 0.078 0.089
AIC 4272.882 4040.485 4815.780 4669.536
BIC 4285.581 4053.007 4828.479 4682.057
Note: This table shows average marginal effects (AMEs) from a probit model of SMEs’ characteristics on the probability of
using COVID-19-related funding. All regressions include a constant term. The base categories for categorical variables are
as follows: zero employees (size), 0–5 years (business age), 18−30 years old (owner’s age), and decreased (turnover change).
Commercial SME is the base category for comparing organizational forms. For the purposes of this study, we will refer to
social enterprises and traditional nonprofits as third-sector organizations (TSOs). However, for the purposes of our empirical
analysis, we will analyze them separately. All models include industry and regional fixed effects. Survey weights applied to
represent the population of SMEs in the United Kingdom. Z-statistics adjusted for clustering at regional level are reported in
parentheses. Statistical significance at the 10%, 5%, and 1% levels are showed by *, **, and ***, respectively.
5 CONCLUSIONS
The COVID-19 pandemic has led to unprecedented challenges for TSOs (nonprofit organizations and social enter-
prises) and socially oriented SMEs, impacting their operations and finances, and posing a threat to their very survival.
Despite limited research on the resilience and adaptability of these types of organizations during the pandemic, it is
crucial to understand their capacity to respond to emerging challenges, maintain their mission, and bounce back from a
crisis quickly and effectively. By being agile and adaptive, these SMEs can take advantage of new opportunities, secure
support, and ensure their long-term sustainability.
This paper seeks to shed light on the challenges faced by TSOs and socially oriented SMEs as a result of the pan-
demic, with the goal of identifying key factors and business characteristics that have enabled these organizations and
businesses to adapt and maintain their operations. Using the most recent waves (2019–2020) of the LSBS survey,
we first assess whether these organizations have perceived the pandemic as a major obstacle. Second, we evalu-
ate the impact of lockdown measures implemented in the United Kingdom, exploring whether they have negatively
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24 LIÑARES-ZEGARRA and WILSON
affected the ability of SMEs to conduct business and potentially hindered their operations. We also examine whether
the COVID-19 pandemic affected a number of future plans in place for these organizations, and forced them to adjust
their strategies and priorities. Finally, we examine the ability of these organizations to obtain COVID-19-related fund-
ing through government-backed loans and grants. Such financial support has been crucial for many organizations and
businesses during the pandemic, and this study assessed the extent to which SMEs have been able to access these
resources.
By employing probit and MNP models to investigate the impact of COVID-19 on businesses across four key
areas, we offer a comprehensive understanding of the challenges faced by businesses during the pandemic and their
responses. Our results suggest that social enterprises were less likely to view the pandemic as a significant obsta-
cle compared to commercial SMEs, while socially focused SMEs were more likely to perceive it as a major obstacle.
Additionally, compared to commercial SMEs, TSOs and socially oriented businesses were more likely to increase
their operations during the lockdown restrictions. The pandemic had varying impacts on the future plans of TSOs
and socially oriented SMEs compared to commercial SMEs. Traditional nonprofits and socially oriented SMEs were
less likely to have their workforce development and leadership plans affected by the pandemic, but they faced more
significant challenges in their plans for capital investment and recruitment of new staff in overseas markets. Social
enterprises, on the other hand, experienced fewer impediments to their plans for introducing new working prac-
tices and developing and launching new products or services, but they faced more significant obstacles in their plans
for research and development investment and increasing export sales or selling to new overseas markets. Finally,
the study revealed that traditional nonprofit and social enterprises were less likely to use coronavirus COVID-19
government-backed accredited loans or finance agreements compared to commercial SMEs, indicating the need for
alternative forms of support or financing to address the economic disruptions caused by the pandemic. Socially ori-
ented SMEs were more likely to use coronavirus COVID-19 business grants funded by the government or local
authorities compared to commercial SMEs.
The empirical results bring forth several insights that are imperative for public policy implementation, particularly
from the perspective of TSOs and socially oriented SMEs during the COVID-19 pandemic. Among the foremost find-
ings is the diminished usage of COVID-19 government-backed accredited loans or finance agreements by traditional
nonprofit organizations and social enterprises as compared to commercial SMEs. This suggests a strong need for alter-
native financial support mechanisms suitable for these organizations during periods of increased uncertainty, such as
the COVID-19 pandemic. This finding has significant implications for public policy, hinting at the potential necessity
to diversify and enhance financial support mechanisms that are not just accessible, but are also appropriate for these
organizations. Such mechanisms could take the form of grants, subsidies, low-interest loans, or other types of support
targeted to organizations with a social mission.
The resilience and adaptability of business models, especially during crises, emerged as another significant point of
consideration. Our study shows that social enterprises demonstrate a higher level of resilience during the pandemic,
perceiving it as less of a major obstacle to business success. On the contrary, socially oriented SMEs were more likely
to see the pandemic as a considerable challenge. This insight might call upon public policy to offer enhanced support
to socially focused SMEs, possibly in the form of resilience-building measures or strategies to enable these businesses
to adapt their models effectively and weather crises more efficiently.
The effect of the pandemic on future business plans was another critical area of focus. The results indicate that
traditional nonprofit organizations and socially oriented SMEs are less likely to adjust their workforce development
and leadership plans. However, they face significant challenges in pursuing capital investment and recruitment plans.
In contrast, social enterprises encounter fewer challenges in implementing new working practices and launching new
services, but are more likely to face difficulties in R&D investment. These insights may warrant targeted support mea-
sures designed to facilitate these specific areas of future business planning. In addition, both socially oriented SMEs
and social enterprises are to be more likely to increase their activities during the pandemic. This suggests that these
organizations have the potential to adapt and maintain operations during crises and therefore are deserving of public
support and investment. Finally, the utilization of COVID-19 business grants by socially oriented SMEs is pronounced
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LIÑARES-ZEGARRA and WILSON 25
compared to their commercial counterparts. This suggests that grant funding can be an effective tool in supporting
these organizations during crises.
Overall, these findings suggest that public policy should strive to provide a diverse range of support mechanisms,
including financial and operational support, tailored to the unique challenges and opportunities confronted by TSOs
and socially oriented SMEs. This involves creating new or expanding existing financial support programs, offering
targeted assistance for strategic planning and business model adaptation, and providing resources to aid these orga-
nizations in increasing their resilience and sustaining or extending their operations during times of crisis. By doing so,
public policy can facilitate the continued success and impact of TSOs and socially oriented SMEs.
NOTES
1
TSOs encompass a diverse range of organizations such as registered charities, cooperatives, social enterprises, or compa-
nies limited by guarantee. They share the goal of pursuing social, environmental, or cultural objectives, are independent
from government, and reinvest surpluses to achieve these objectives (Bourne, 2005). A detailed discussion of the different
organizational forms used in the present study is included in Section 2 and Figure 1.
2
In February 2023, the Department of Business, Energy and Industrial Strategy was divided into three constituent depart-
ments, comprising: the Department for Business and Trade; the Department for Energy Security and Net Zero; and the
Department for Science, Innovation and Technology.
3
Resilience (as described in recent studies such as Barbera et al. [2020], Kober & Thambar [2021; and Plaisance [2022]) is
defined as the ability of organizations to bounce back from crises.
4
Some (but not all) social enterprises have charitable status. The distinguishing feature of a social enterprise is the proportion
of turnover derived from trading being above 50%. Therefore, for this study, the term “traditional nonprofit” is used to indi-
cate a charity that earns under 50% of its revenue from commercial activity. The definition used in this paper is close to the
OECD’s (1999) definition, where social enterprises are characterized by a special organizational form and serve a distinct
function as follows: ‘. . . any private activity conducted in the public interest, organized with an entrepreneurial strategy, whose main
purpose is not the maximization of profit but the attainment of certain economic and social goals, and which has the capacity for
bringing innovative solutions to the problems of social exclusion and unemployment.’
ACKNOWLEDGMENTS
We are grateful to Barbara Casu, Laura Chiaramonte, Douglas Cumming, Franco Fiordelisi, Claudia Girardone, Sotirios
Kokas, Alessandra Mongiardino, and other participants at the Workshop on Social Responsibility, Banks and Mar-
kets During the COVID-19 Crisis and Beyond for useful comments and suggestions. We gratefully acknowledge the
ESRC-funded Enterprise Research Centre and the Department for Business, Energy and Industrial Strategy (BEIS) for
funding for this study.
DATA AVAILABILITY STATEMENT
The data that support the findings of this study are available on request from the corresponding author. The data are
not publicly available due to privacy or ethical restrictions.
REFERENCES
Bacq, S., & Lumpkin, G. T. (2021). Social entrepreneurship and COVID-19. Journal of Management Studies, 58, 285–288.
Barbera, C., Guarini, E., & Steccolini, I. (2020). How do governments cope with austerity? The roles of accounting in shaping
governmental financial resilience. Accounting, Auditing & Accountability Journal, 33, 529–558.
Belitski, M., Guenther, C., Kritikos, A. S., & Thurik, R. (2022). Economic effects of the COVID-19 pandemic on entrepreneurship
and small businesses. Small Business Economics, 58, 593–609.
Bloom, N., Fletcher, R. S., & Yeh, E. (2021). The impact of COVID-19 on US firms. National Bureau of Economic Research Working
Paper Series, No. 28314.
Bourne, J. (2005). Working with the third sector. National Audit Office.
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: Methods and applications. Cambridge University Press.
Cornforth, C. (2014). Understanding and combating mission drift in social enterprises. Social Enterprise Journal, 10, 3–20.
Davies, I. A., Haugh, H., & Chambers, L. (2019). Barriers to social enterprise growth. Journal of Small Business Management, 57,
1616–1636.
Department for Business Energy and Industrial Strategy. (2022). Longitudinal Small Business Survey, 2015–2020. Author.
14680408, 0, Downloaded from https://onlinelibrary.wiley.com/doi/10.1111/faam.12373 by Test, Wiley Online Library on [28/06/2023]. See the Terms and Conditions (https://onlinelibrary.wiley.com/terms-and-conditions) on Wiley Online Library for rules of use; OA articles are governed by the applicable Creative Commons License
26 LIÑARES-ZEGARRA and WILSON
Dickerson, C., & Hassanien, A. (2018). Restaurants’ social enterprise business model: Three case studies. Journal of Quality
Assurance in Hospitality & Tourism, 19, 192–216.
Doherty, B., Haugh, H., & Lyon, F. (2014). Social enterprises as hybrid organizations: A review and research agenda. International
Journal of Management Reviews, 16, 417–436.
Green, E., Ritchie, F., Bradley, P., & Parry, G. (2021). Financial resilience, income dependence and organisational survival in UK
charities. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 32, 992–1008.
Haugh, H., Robson, P., Hagedoorn, J., & Sugar, K. (2022). The nascent ecology of social enterprise. Small Business Economics, 58,
1223–1242.
Hurley, J., Karmakar, S., Markoska, E., Walczak, E., & Walker, D. (2021). Impacts of the Covid-19 crisis: Evidence from 2 million
UK SMEs. Bank of England Working Paper No. 924.
Hyndman, N. (2018). The Charities SORP: An ‘engine’ for good? Public Money & Management, 38, 247–250.
Hyndman, N. (2020). UK charities and the pandemic: Navigating the perfect storm. Journal of Accounting & Organizational
Change, 16, 587–592.
Kalemli-Ozcan, S., Gourinchas, P.-O., Penciakova, V., & Sander, N. (2020). COVID-19 and SME failures. IMF Working Papers,
2020, A001.
Kober, R., & Thambar, P. J. (2021). Coping with COVID-19: The role of accounting in shaping charities’ financial resilience.
Accounting, Auditing & Accountability Journal, 34, 1416–1429.
Lee, N., & Cowling, M. (2013). Place, sorting effects and barriers to enterprise in deprived areas: Different problems or
different firms? International Small Business Journal, 31, 914–937.
Liu, G., & Ko, W.-W. (2012). Organizational learning and marketing capability development: A study of the charity retailing
operations of British social enterprise. Nonprofit and Voluntary Sector Quarterly, 41, 580–608.
Lyon, F., & Owen, R. (2019). Financing social enterprises and the demand for social investment. Strategic Change, 28, 47–57.
Magrizos, S., Apospori, E., Carrigan, M., & Jones, R. (2021). Is CSR the panacea for SMEs? A study of socially responsible SMEs
during economic crisis. European Management Journal, 39, 291–303.
Mathibe, M. S., Chinyamurindi, W. T., & Hove-Sibanda, P. (2023). Value co-creation as a mediator between strategic planning
and social enterprise performance. Social Enterprise Journal, 19(1), 1750–8614.
Murphy, P. J., & Coombes, S. M. (2009). A model of social entrepreneurial discovery. Journal of Business Ethics, 87, 325–336.
Organisation for Economic Co-operation and Development (OECD). 1999. Social enterprises. Author.
Pape, U., Brandsen, T., Pahl, J. B., Pieliński, B., Baturina, D., Brookes, N., Chaves-Ávila, R., Kendall, J., Matančević, J., Petrella, F.,
Rentzsch, C., Richez-Battesti, N., Savall-Morera, T., Simsa, R., & Zimmer, A. (2020). Changing policy environments in Europe
and the resilience of the third sector. VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 31, 238–249.
Plaisance, G. (2022). Resilience in arts and cultural nonprofit organizations: An analysis of the Covid-19 crisis in France.
VOLUNTAS: International Journal of Voluntary and Nonprofit Organizations, 33, 1015–1034.
Pro Bono Economics. (2020). Press release: Charities facing £10.1 billion funding gap over the next six months. Author.
Social Enterprise UK. (2018). The hidden revolution. Author.
Weaver, R., & Blakey, C. L. (2022). Winter always comes: Social enterprise in times of crisis. Social Enterprise Journal, 18, 489–
502.
Weaver, R. L. (2020). The impact of COVID-19 on the social enterprise sector. Journal of Social Entrepreneurship, 14(2), 177–
185.
Wellalage, N. H., Kumar, V., Hunjra, A. I., & Al-Faryan, M. A. S. (2022). Environmental performance and firm financing during
COVID-19 outbreaks: Evidence from SMEs. Finance Research Letters, 47, 102568.
How to cite this article: Liñares-Zegarra, J. M., & Wilson, J. O. S. (2023). Navigating uncertainty: The
resilience of third-sector organizations and socially oriented small- and medium-sized enterprises during the
COVID-19 pandemic. Financial Accountability & Management, 1–26. https://doi.org/10.1111/faam.12373
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