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Navigating uncertainty: The resilience of third‐sector organizations and socially oriented small‐ and medium‐sized enterprises during the COVID‐19 pandemic

Navigating uncertainty: The resilience of third‐sector organizations and socially oriented small‐... INTRODUCTIONThird‐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, practitioners, 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 enterprises, 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 enterprises. 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 critical 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.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 operations; (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 enterprises (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 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 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 introducing 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 remarkable 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 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.LITERATUREThis 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 charities 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 organizations 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.3The 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 significantly 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 organizations 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 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 during 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 (Cornforth, 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 pandemic, 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.DATA AND METHODOLOGYMapping the socially responsible SME ecosystemThe 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 understanding 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.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 generate 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.1FIGUREDecision 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.longitudinal Small Business SurveyThe 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.1TABLEVariable definition.VariableDefinitionLSBS codeMajor obstacleWhich of the following would you say are major obstacles to the success of your business in general?G2KCOVID‐19COVID‐19 as a major obstacle for the businessBusiness impact during lockdownWhich of the following statements best describes how your [ANSWER AT A‐2] adapted during the lockdown restrictions from the end of March to the middle of June 2020?GC1Closed down or reduced operationsClosed down completely (temporarily) or operations were reducedUnaffectedUnaffected by COVID‐19 restrictionsIncreased operationsOperations were increasedFuture plans affected by COVID‐19Have plans [over the next 3 years] been affected by the coronavirus COVID‐19 pandemic? If Yes: Which plans?R4A(A‐J)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 fundingHas your company used any …?H100A_1 and H100A_2COVID‐funding (loans)Coronavirus COVID‐19 government‐backed accredited loans or finance agreements such as Coronavirus Business Interruption Loan and Bounce Back LoanCOVID‐funding (grants)Coronavirus COVID‐19 business grants funded by government or local authoritySME classificationSOCENTCommercial SMEs (base category)Traditional nonprofitSocial enterpriseSocially orientated SMESee Figure 1. 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.Aims to growAim to grow sales over the next 3 years.R1SizeA2SPSS1Zero employees (base category)Zero employee business had no employees on their payroll (excluding owners and partners) at the time of the interview.Micro1–9 employees.Small10–49 employees.Medium50–249 employees.Business ageAge of the firm.A6SUM and A6, missing values for 2016 are completed with values from 20150–5 years (base category)6–10 years11–20 years20+ yearsTurnover changeTurnover in the past 12 months, compared with the previous 12 months.P2Decreased (base category)Stayed roughly the sameIncreasedProfitFirm generates a profit or surplus after considering all sources of income in the last financial year.P12Urban areaBroad urban/rural categorization from postcode.URBRUR2Female ledBusiness is women led.WLEDMinority ethnic ledBusiness is MEG led.MLEDFamily ownedBusiness is a family‐owned business (i.e., one that is majority owned by members of the same family).A12Business planThe business has a formal written business plan.F5RegionRegion where the firm has its headquarters.NATIONEngland (base category)ScotlandWalesNorthern IrelandBroad sectorIndustry sectorSECTORManufacturing sector (base category)Production and construction (SIC 2007: ABCDEF).Transportation and retail servicesTransport, retail, and food service/accommodation (SIC 2007: GHI).Business servicesBusiness services (SIC 2007: JKLMN).Other servicesOther 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.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% 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 experienced 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 agreements, 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.2TABLESummary statistics.MeanSDNMAJOR OBSTACLECOVID‐190.7040.4572516BUSINESS IMPACT DURING LOCKDOWNClosed down reduced operations0.7850.4117596Unaffected0.1560.3627596Increased operations0.0600.2377596SMEs WITH PLANS AFFECTED BY COVID‐19A: Increase the skills of the workforce0.4240.9051270B: Increase the leadership capability of managers0.3890.966874C: Capital investment (in premises, machinery, etc.) in the United Kingdom0.5070.614764D: Capital investment (in premises, machinery, etc.) in overseas markets0.6130.589125E: Develop and launch new products/services0.5520.521892F: Introduce new working practices0.4770.5791024G: Invest in R&D0.4280.639629H: Increase export sales or begin selling to new overseas markets0.5400.565388I: Recruitment of new staff in the United Kingdom0.4980.6881279J: Recruitment of new staff in overseas offices0.4980.660116USE OF COVID FUNDINGCOVID‐funding (loans)0.2760.6157462COVID‐funding (grants)0.3120.6297445TYPES OF ORGANIZATIONS AND BUSINESSESCommercial SME (base category)0.6800.46710,883Traditional nonprofit0.0420.20110,883Social enterprise0.0820.27410,883Socially orientated SME0.1960.39710,883CONTROL VARIABLESEntrepreneur orientationAims to grow0.5850.49318,621SizeZero employees (base category)0.7590.42718,621Micro (1–9)0.1980.39918,621Small (10–49)0.0360.18718,621Medium (50–249)0.0060.07718,621Business age0–5 years (base category)0.1360.34318,5596–10 years0.1840.38718,55911–20 years0.2980.45818,55920+ years0.3820.48618,559Turnover changeDecreased (base category)0.3830.48617,897Stayed the same0.3920.48817,897Increased0.2260.41817,897ProfitabilityProfit0.7640.42517,648Business characteristicsUrban area0.6860.46418,100Family owned0.8580.34918,573Business plan0.2830.45117,966Female led0.1860.38917,757Minority ethnic led0.0450.20817,258RegionEngland (base category)0.8840.32018,621Scotland0.0590.23618,621Wales0.0350.18318,621Northern Ireland0.0220.14818,621Broad sectorManufacturing sector (base category)0.2480.43218,621Transportation and retail services0.1880.39118,621Business services0.3370.47318,621Other services0.2270.41918,621Note: 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.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 businesses 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, 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 business 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 correlation is 0.33 (between size and business plan dummy). Hence, multicollinearity does not appear to present a critical concern for our analysis.3TABLECorrelation matrix.Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(1) Social enterprise1.000(2) Aim to grow0.027*1.000(3) Firm size0.051*0.246*1.000(4) Firm age−0.020−0.091*0.179*1.000(5) Turnover change0.0170.075*0.131*−0.055*1.000(6) Profit−0.067*−0.0020.056*0.020*0.250*1.000(7) Urban−0.0160.058*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 plan0.112*0.177*0.328*–0.0130.070*–0.034*0.053*–0.206*1.000(10) Women led0.035*–0.019–0.028*–0.046*–0.040*–0.051*–0.0020.059*0.0041.000(11) MEG led0.041*0.032*0.013−0.078*−0.007−0.032*0.096*0.0020.030*–0.0021.000(12) Region0.027*0.0030.016−0.027*0.0110.022*−0.099*0.020*0.003−0.004−0.055*1.000(13) Broad Sector0.092*–0.060*–0.063*–0.040*–0.019–0.079*0.133*−0.185*0.084*0.172*0.043*−0.067*1.000Note: This table reports the correlation matrix between all variables used in this study.*Statistical significance at the 1% level.MethodologyThe 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.Multinomial Probit (MNP) modelWe use an MNP regression to investigate how TSOs and socially oriented SMEs have adapted their business operations 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 as1Uij=xij′β+εij,$$\begin{equation}{U_{ij}} = {x^{\prime}_{ij}}\beta + {\varepsilon _{ij}},\end{equation}$$where xij′${x^{\prime}_{ij}}$ 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 is2pij=Pryi=j=Prεik−εij≤xij−xik′β,forallk,$$\begin{equation}{p_{ij}} = \Pr \left( {{y_i} = j} \right) = \Pr \left\{ {{\varepsilon _{ik}} - {\varepsilon _{ij}} \le {{\left( {{x_{ij}} - {x_{ik}}} \right)}^\prime }\beta } \right\},{\mathrm{for all}}\,k,\end{equation}$$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.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 organizational forms. Standard errors are clustered at regional level to allow for individual correlations within the same geographic area.Probit modelProbit 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.3PrObstacles/FuturePlans/COVID−−19Fundingi=1=ΦXiβ+vi,$$\begin{equation} \operatorname{\mathrm{Pr}}\left(\textit{Obst}\textit{acles}/\textit{Futu}\textit{re}\, \textit{Plans}/\textit{COVID}--19\textit{Fund}\textit{in}{g}_{i}=1\right)=\Phi\left({X}_{i}\beta +{v}_{i}\right), \end{equation}$$where νi are independent and identically distributed (i.i.d., N(0,σν2)$N( {0,\sigma _\nu ^2} )$, and Φ is the standard normal cumulative distribution 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.RESULTS AND DISCUSSIONIn 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 organizations 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.COVID‐19 as a major obstacle to business successTable 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 variables, 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 significant 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 models, 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.4TABLECOVID‐19 pandemic as major obstacles to the success of your business in general.Model 1Model 2Traditional nonprofitt – 10.0350.063(0.75)(1.33)Social enterpriset – 1–0.025***–0.033***(−11.41)(−10.39)Socially oriented SMEt – 10.042***0.038***(3.17)(4.34)Aims to growt – 1–0.044*–0.049**(−1.80)(−2.28)Size: Micro0.026***0.021***(3.65)(3.03)Size: Small0.0100.022(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 years0.0130.016***(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 – 10.0000.016(0.02)(0.97)Locationt: Urban area0.067***0.075***(3.29)(3.66)Family owned0.0180.032(0.45)(0.70)Business plan0.0510.052(1.63)(1.61)Female ledt – 10.066(1.19)Minority ethnic ledt – 10.007(0.33)Fixed effectsRegional FEsYesYesIndustry FEsYesYesObservations14241317Log pseudo‐likelihood−836.401−798.935R20.0670.067AIC1676.8011601.869BIC1687.3241612.236Note: 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.Impact of government‐imposed lockdown restrictions on business operationsTable 5 provides additional information regarding how TSOs and socially oriented SMEs adapted their operations during 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.5TABLEWhich 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 completely (temporarily) or Operations were reducedUnaffected by COVID‐19 restrictionsOperations were increasedTraditional nonprofitt – 10.029–0.099***0.070***(0.71)(−3.89)(4.52)Social enterpriset – 1–0.021–0.0160.037***(−1.15)(−0.69)(5.60)Socially oriented SMEt – 1–0.016–0.0040.020***(−0.96)(−0.25)(18.50)Aims to growt – 10.044***–0.057***0.013**(2.84)(−5.22)(2.48)Size: Micro–0.006–0.0090.015***(−0.58)(−0.73)(2.66)Size: Small–0.008–0.0210.030***(−0.51)(−1.31)(7.99)Size: Medium–0.040*–0.0070.047***(−1.92)(−0.42)(12.90)Business age: 6–10 years–0.0120.026**–0.014***(−0.86)(2.34)(−4.29)Business age: 11–20 years0.0070.014–0.021***(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 – 10.044***–0.044***0.001(5.92)(−6.89)(0.51)Locationt: Urban area0.031***–0.040***0.009**(4.50)(−9.69)(2.02)Family owned0.014–0.055***0.041***(1.22)(−3.60)(8.12)Business plan0.007–0.024***0.018***(1.25)(−2.92)(3.84)Female ledt – 10.101***–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)Fixed effectsRegional FEsYesIndustry FEsYesObservations3892Log likelihood–2443.91AIC4891.835BIC4904.368Note: 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.Impact of the COVID‐19 pandemic on future business plansThe 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 development 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, machinery, 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 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.6TABLESMEs with future plans over the next 3 years affected by COVID‐19 pandemic.Increase the skills of the workforceIncrease the leadership capability of managersCapital investment in United KingdomCapital investment in overseas marketDevelop and launch new products/servicesTraditional nonprofitt – 1−0.237***−0.1220.043n.e0.096**(−7.90)(−1.43)(0.68)(.)(2.30)Social enterpriset – 10.020−0.143***−0.314***0.586***−0.033*(0.87)(−4.95)(−30.00)(907.06)(−1.80)Socially oriented SMEt – 1−0.030*−0.107**−0.070*0.547***0.051(−1.92)(−1.99)(−1.70)(21.76)(0.99)Aims to growt – 1−0.064***0.042**0.140***−0.147***0.106**(−4.84)(1.97)(31.50)(−16.21)(2.19)Size: Micron.en.e0.081***0.017–0.112***(.)(.)(2.59)(0.90)(−5.48)Size: Small0.0140.0020.151***−0.097***−0.058*(0.62)(0.06)(2.66)(−30.95)(−1.69)Size: Medium0.106***0.127***0.143***0.064**−0.101(4.09)(3.99)(3.58)(2.22)(−1.30)Business age: 6–10 years−0.128***0.100***−0.114**0.531***−0.267(−27.26)(4.54)(−2.51)(49.75)(−1.45)Business age: 11–20 years−0.108***−0.089−0.102***0.575***−0.082(−3.43)(−1.15)(−10.74)(14.44)(−0.62)Business age: 20+ years−0.068***0.066−0.269***−0.124***−0.220**(−7.46)(1.58)(−5.61)(−6.81)(−2.48)Turnover change (stayed the same)t – 1–0.076*–0.090***–0.031***–0.387***–0.042(−1.66)(−2.95)(−3.90)(−19.01)(−1.26)Turnover change (increased)t – 1–0.085***–0.098***0.033–0.278***–0.033***(−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: Urban area0.002–0.0150.012–0.230***0.006(0.07)(−0.55)(0.18)(−3.11)(0.24)Family owned0.090***–0.0280.077***–0.431***0.017(8.51)(−1.17)(4.24)(−20.06)(0.72)Business plan0.052***–0.074***–0.002–0.090***0.137***(141.83)(−4.94)(−0.21)(−5.36)(41.32)Female ledt – 10.037*0.0470.1210.282***0.115***(1.92)(0.53)(0.95)(3.54)(5.78)Minority ethnic ledt – 10.141***0.151***0.005–0.863***–0.050**(15.26)(22.90)(0.28)(−32.28)(−2.36)Fixed effectsRegional FEsYesYesYesYesYesIndustry FEsYesYesYesYesYesObservations67245139958456Log pseudo‐likelihood−142.109−77.933−148.533−8.296−283.218R20.0430.0650.1660.6660.072AIC288.219159.866301.06718.591570.435BIC297.239168.089309.04520.652578.680Note: This table shows average marginal effects from probit regressions. 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. 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.7TABLESMEs with future plans over the next 3 years affected by COVID‐19 pandemic.Introduce new working practicesInvest in R&DIncrease export sales or begin selling to new overseas marketsRecruitment of new staff in the United KingdomRecruitment of new staff in overseas officesTraditional nonprofitt – 1−0.135−0.068***n.e0.038−0.013(−0.87)(−4.12)(.)(0.81)(−0.18)Social enterpriset – 1–0.186***0.186***0.535***0.042***–0.121**(−6.28)(6.60)(12.95)(4.09)(−2.46)Socially oriented SMEt – 10.0890.174***0.047*0.106***0.242**(1.48)(5.78)(1.70)(9.26)(2.43)Aims to growt – 10.102**0.0320.0030.066***–0.060(2.55)(0.92)(0.04)(5.36)(−0.56)Size: Micro–0.035–0.0400.070*–0.109***0.467***(−1.03)(−0.88)(1.87)(−6.86)(5.82)Size: Small–0.058–0.0320.078–0.148***0.236***(−0.51)(−0.85)(0.99)(−7.38)(2.99)Size: Medium0.074–0.006–0.018–0.077***0.336(0.64)(−0.09)(−0.26)(−2.89)(1.62)Business age: 6–10 years0.110–0.453***–0.239***–0.147***0.012(0.97)(−12.41)(−25.06)(−9.22)(0.20)Business age: 11–20 years0.092**–0.270***0.026–0.091***0.143***(2.46)(−35.09)(1.56)(−14.94)(3.22)Business age: 20+ years0.048***–0.259***–0.170***–0.138***–0.010(3.07)(−5.96)(−3.23)(−28.14)(−0.08)Turnover change (stayed the same)t – 10.003–0.111***–0.217***–0.110***–0.293***(0.06)(−36.09)(−145.77)(−9.91)(−39.07)Turnover change (increased)t – 1–0.078*–0.193***–0.232***–0.167***–0.206***(−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)Locationt: Urban area0.137***0.0450.052***–0.052***0.139*(3.10)(1.21)(49.96)(−8.16)(1.85)Family owned0.030***0.085***–0.028**0.072***–0.062**(3.39)(5.16)(−1.98)(8.33)(−2.22)Business plan0.0200.129***–0.090***0.138***–0.016***(0.70)(12.28)(−6.19)(17.39)(−2.84)Female ledt – 10.034–0.170***–0.053***–0.066***0.131***(0.23)(−15.32)(−8.56)(−3.94)(2.70)Minority ethnic ledt – 1–0.130**0.043***–0.182***–0.155***0.014**(−2.51)(3.22)(−15.97)(−23.41)(2.30)Fixed effectsRegional FEsYesYesYesYesYesIndustry FEsYesYesYesYesYesObservations51133320765866Log pseudo‐likelihood–243.464–108.114–91.832–220.690–7.739R20.1530.2280.2050.0720.649AIC490.928220.228187.665445.38117.479BIC499.401227.844194.330454.35919.668Note: This table shows average marginal effects from probit regressions. 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. 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.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 enterprises, 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 investment 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.Access and use of COVID‐19 fundingFinally, 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 Interruption 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 subsidies, streamlined administrative processes for accessing specific type of funding, or other forms of financial assistance. 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.8TABLEUse 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 Back LoanCoronavirus COVID‐19 business grants funded by government or local authorityModel 1Model 2Model 3Model 4Traditional nonprofitt – 1–0.188***–0.204***–0.079–0.053***(−37.56)(−26.04)(−1.23)(−5.97)Social enterpriset – 1–0.044**–0.041**0.021**0.026(−2.51)(−2.19)(2.25)(1.59)Socially oriented SMEt – 1–0.007–0.0060.019**0.006***(−0.77)(−0.68)(2.46)(4.56)Aims to growt – 10.124***0.126***0.093***0.055***(26.63)(34.62)(6.09)(3.26)Size: Micro0.153***0.151***0.174***0.209***(4.97)(4.65)(293.09)(23.54)Size: Small0.181***0.169***0.117***0.142***(5.20)(4.82)(7.94)(10.66)Size: Medium0.131***0.120***–0.039***–0.003(5.80)(5.60)(−2.99)(−0.59)Business age: 6–10 years0.0100.026–0.0600.003(0.65)(1.44)(−1.61)(0.35)Business age: 11–20 years–0.0110.003–0.032–0.012(−1.21)(0.35)(−0.78)(−0.97)Business age: 20+ years–0.075***–0.057**–0.022–0.027***(−4.00)(−2.51)(−0.43)(−6.56)Turnover change (stayed the same)t – 10.0040.008–0.053***0.015(0.30)(0.54)(−2.88)(0.54)Turnover change (increased)t – 1–0.012–0.006–0.049–0.006(−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 area–0.003–0.008–0.0210.010(−0.20)(−0.48)(−1.40)(0.62)Family owned0.0380.021–0.049***0.052***(1.58)(0.69)(−4.17)(12.58)Business plan0.026***0.031***–0.0050.014*(4.45)(4.49)(−0.76)(1.70)Female ledt – 1–0.054***0.025***(−4.67)(2.74)Minority ethnic ledt – 10.058***0.034***(2.79)(7.17)Fixed effectsRegional FEsYesYesYesYesIndustry FEsYesYesYesYesObservations4229387042273868Log pseudo‐likelihood–2134.441–2018.242–2405.890–2332.768R20.0810.0820.0780.089AIC4272.8824040.4854815.7804669.536BIC4285.5814053.0074828.4794682.057Note: 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.CONCLUSIONSThe COVID‐19 pandemic has led to unprecedented challenges for TSOs (nonprofit organizations and social enterprises) 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 pandemic, 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 evaluate the impact of lockdown measures implemented in the United Kingdom, exploring whether they have negatively 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 funding 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 obstacle 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 practices 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 oriented 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 findings 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 alternative 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 measures 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 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 organizations 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.ENDNOTES1TSOs encompass a diverse range of organizations such as registered charities, cooperatives, social enterprises, or companies 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 1.2In February 2023, the Department of Business, Energy and Industrial Strategy was divided into three constituent departments, comprising: the Department for Business and Trade; the Department for Energy Security and Net Zero; and the Department for Science, Innovation and Technology.3Resilience (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.4Some (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 indicate 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.’ACKNOWLEDGMENTSWe 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 Markets 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 STATEMENTThe 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.REFERENCESBacq, 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.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. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Financial Accountability and Management Wiley

Navigating uncertainty: The resilience of third‐sector organizations and socially oriented small‐ and medium‐sized enterprises during the COVID‐19 pandemic

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INTRODUCTIONThird‐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, practitioners, 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 enterprises, 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 enterprises. 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 critical 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.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 operations; (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 enterprises (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 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 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 introducing 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 remarkable 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 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.LITERATUREThis 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 charities 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 organizations 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.3The 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 significantly 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 organizations 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 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 during 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 (Cornforth, 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 pandemic, 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.DATA AND METHODOLOGYMapping the socially responsible SME ecosystemThe 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 understanding 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.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 generate 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.1FIGUREDecision 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.longitudinal Small Business SurveyThe 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.1TABLEVariable definition.VariableDefinitionLSBS codeMajor obstacleWhich of the following would you say are major obstacles to the success of your business in general?G2KCOVID‐19COVID‐19 as a major obstacle for the businessBusiness impact during lockdownWhich of the following statements best describes how your [ANSWER AT A‐2] adapted during the lockdown restrictions from the end of March to the middle of June 2020?GC1Closed down or reduced operationsClosed down completely (temporarily) or operations were reducedUnaffectedUnaffected by COVID‐19 restrictionsIncreased operationsOperations were increasedFuture plans affected by COVID‐19Have plans [over the next 3 years] been affected by the coronavirus COVID‐19 pandemic? If Yes: Which plans?R4A(A‐J)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 fundingHas your company used any …?H100A_1 and H100A_2COVID‐funding (loans)Coronavirus COVID‐19 government‐backed accredited loans or finance agreements such as Coronavirus Business Interruption Loan and Bounce Back LoanCOVID‐funding (grants)Coronavirus COVID‐19 business grants funded by government or local authoritySME classificationSOCENTCommercial SMEs (base category)Traditional nonprofitSocial enterpriseSocially orientated SMESee Figure 1. 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.Aims to growAim to grow sales over the next 3 years.R1SizeA2SPSS1Zero employees (base category)Zero employee business had no employees on their payroll (excluding owners and partners) at the time of the interview.Micro1–9 employees.Small10–49 employees.Medium50–249 employees.Business ageAge of the firm.A6SUM and A6, missing values for 2016 are completed with values from 20150–5 years (base category)6–10 years11–20 years20+ yearsTurnover changeTurnover in the past 12 months, compared with the previous 12 months.P2Decreased (base category)Stayed roughly the sameIncreasedProfitFirm generates a profit or surplus after considering all sources of income in the last financial year.P12Urban areaBroad urban/rural categorization from postcode.URBRUR2Female ledBusiness is women led.WLEDMinority ethnic ledBusiness is MEG led.MLEDFamily ownedBusiness is a family‐owned business (i.e., one that is majority owned by members of the same family).A12Business planThe business has a formal written business plan.F5RegionRegion where the firm has its headquarters.NATIONEngland (base category)ScotlandWalesNorthern IrelandBroad sectorIndustry sectorSECTORManufacturing sector (base category)Production and construction (SIC 2007: ABCDEF).Transportation and retail servicesTransport, retail, and food service/accommodation (SIC 2007: GHI).Business servicesBusiness services (SIC 2007: JKLMN).Other servicesOther 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.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% 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 experienced 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 agreements, 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.2TABLESummary statistics.MeanSDNMAJOR OBSTACLECOVID‐190.7040.4572516BUSINESS IMPACT DURING LOCKDOWNClosed down reduced operations0.7850.4117596Unaffected0.1560.3627596Increased operations0.0600.2377596SMEs WITH PLANS AFFECTED BY COVID‐19A: Increase the skills of the workforce0.4240.9051270B: Increase the leadership capability of managers0.3890.966874C: Capital investment (in premises, machinery, etc.) in the United Kingdom0.5070.614764D: Capital investment (in premises, machinery, etc.) in overseas markets0.6130.589125E: Develop and launch new products/services0.5520.521892F: Introduce new working practices0.4770.5791024G: Invest in R&D0.4280.639629H: Increase export sales or begin selling to new overseas markets0.5400.565388I: Recruitment of new staff in the United Kingdom0.4980.6881279J: Recruitment of new staff in overseas offices0.4980.660116USE OF COVID FUNDINGCOVID‐funding (loans)0.2760.6157462COVID‐funding (grants)0.3120.6297445TYPES OF ORGANIZATIONS AND BUSINESSESCommercial SME (base category)0.6800.46710,883Traditional nonprofit0.0420.20110,883Social enterprise0.0820.27410,883Socially orientated SME0.1960.39710,883CONTROL VARIABLESEntrepreneur orientationAims to grow0.5850.49318,621SizeZero employees (base category)0.7590.42718,621Micro (1–9)0.1980.39918,621Small (10–49)0.0360.18718,621Medium (50–249)0.0060.07718,621Business age0–5 years (base category)0.1360.34318,5596–10 years0.1840.38718,55911–20 years0.2980.45818,55920+ years0.3820.48618,559Turnover changeDecreased (base category)0.3830.48617,897Stayed the same0.3920.48817,897Increased0.2260.41817,897ProfitabilityProfit0.7640.42517,648Business characteristicsUrban area0.6860.46418,100Family owned0.8580.34918,573Business plan0.2830.45117,966Female led0.1860.38917,757Minority ethnic led0.0450.20817,258RegionEngland (base category)0.8840.32018,621Scotland0.0590.23618,621Wales0.0350.18318,621Northern Ireland0.0220.14818,621Broad sectorManufacturing sector (base category)0.2480.43218,621Transportation and retail services0.1880.39118,621Business services0.3370.47318,621Other services0.2270.41918,621Note: 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.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 businesses 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, 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 business 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 correlation is 0.33 (between size and business plan dummy). Hence, multicollinearity does not appear to present a critical concern for our analysis.3TABLECorrelation matrix.Variables(1)(2)(3)(4)(5)(6)(7)(8)(9)(10)(11)(12)(13)(1) Social enterprise1.000(2) Aim to grow0.027*1.000(3) Firm size0.051*0.246*1.000(4) Firm age−0.020−0.091*0.179*1.000(5) Turnover change0.0170.075*0.131*−0.055*1.000(6) Profit−0.067*−0.0020.056*0.020*0.250*1.000(7) Urban−0.0160.058*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 plan0.112*0.177*0.328*–0.0130.070*–0.034*0.053*–0.206*1.000(10) Women led0.035*–0.019–0.028*–0.046*–0.040*–0.051*–0.0020.059*0.0041.000(11) MEG led0.041*0.032*0.013−0.078*−0.007−0.032*0.096*0.0020.030*–0.0021.000(12) Region0.027*0.0030.016−0.027*0.0110.022*−0.099*0.020*0.003−0.004−0.055*1.000(13) Broad Sector0.092*–0.060*–0.063*–0.040*–0.019–0.079*0.133*−0.185*0.084*0.172*0.043*−0.067*1.000Note: This table reports the correlation matrix between all variables used in this study.*Statistical significance at the 1% level.MethodologyThe 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.Multinomial Probit (MNP) modelWe use an MNP regression to investigate how TSOs and socially oriented SMEs have adapted their business operations 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 as1Uij=xij′β+εij,$$\begin{equation}{U_{ij}} = {x^{\prime}_{ij}}\beta + {\varepsilon _{ij}},\end{equation}$$where xij′${x^{\prime}_{ij}}$ 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 is2pij=Pryi=j=Prεik−εij≤xij−xik′β,forallk,$$\begin{equation}{p_{ij}} = \Pr \left( {{y_i} = j} \right) = \Pr \left\{ {{\varepsilon _{ik}} - {\varepsilon _{ij}} \le {{\left( {{x_{ij}} - {x_{ik}}} \right)}^\prime }\beta } \right\},{\mathrm{for all}}\,k,\end{equation}$$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.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 organizational forms. Standard errors are clustered at regional level to allow for individual correlations within the same geographic area.Probit modelProbit 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.3PrObstacles/FuturePlans/COVID−−19Fundingi=1=ΦXiβ+vi,$$\begin{equation} \operatorname{\mathrm{Pr}}\left(\textit{Obst}\textit{acles}/\textit{Futu}\textit{re}\, \textit{Plans}/\textit{COVID}--19\textit{Fund}\textit{in}{g}_{i}=1\right)=\Phi\left({X}_{i}\beta +{v}_{i}\right), \end{equation}$$where νi are independent and identically distributed (i.i.d., N(0,σν2)$N( {0,\sigma _\nu ^2} )$, and Φ is the standard normal cumulative distribution 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.RESULTS AND DISCUSSIONIn 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 organizations 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.COVID‐19 as a major obstacle to business successTable 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 variables, 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 significant 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 models, 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.4TABLECOVID‐19 pandemic as major obstacles to the success of your business in general.Model 1Model 2Traditional nonprofitt – 10.0350.063(0.75)(1.33)Social enterpriset – 1–0.025***–0.033***(−11.41)(−10.39)Socially oriented SMEt – 10.042***0.038***(3.17)(4.34)Aims to growt – 1–0.044*–0.049**(−1.80)(−2.28)Size: Micro0.026***0.021***(3.65)(3.03)Size: Small0.0100.022(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 years0.0130.016***(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 – 10.0000.016(0.02)(0.97)Locationt: Urban area0.067***0.075***(3.29)(3.66)Family owned0.0180.032(0.45)(0.70)Business plan0.0510.052(1.63)(1.61)Female ledt – 10.066(1.19)Minority ethnic ledt – 10.007(0.33)Fixed effectsRegional FEsYesYesIndustry FEsYesYesObservations14241317Log pseudo‐likelihood−836.401−798.935R20.0670.067AIC1676.8011601.869BIC1687.3241612.236Note: 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.Impact of government‐imposed lockdown restrictions on business operationsTable 5 provides additional information regarding how TSOs and socially oriented SMEs adapted their operations during 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.5TABLEWhich 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 completely (temporarily) or Operations were reducedUnaffected by COVID‐19 restrictionsOperations were increasedTraditional nonprofitt – 10.029–0.099***0.070***(0.71)(−3.89)(4.52)Social enterpriset – 1–0.021–0.0160.037***(−1.15)(−0.69)(5.60)Socially oriented SMEt – 1–0.016–0.0040.020***(−0.96)(−0.25)(18.50)Aims to growt – 10.044***–0.057***0.013**(2.84)(−5.22)(2.48)Size: Micro–0.006–0.0090.015***(−0.58)(−0.73)(2.66)Size: Small–0.008–0.0210.030***(−0.51)(−1.31)(7.99)Size: Medium–0.040*–0.0070.047***(−1.92)(−0.42)(12.90)Business age: 6–10 years–0.0120.026**–0.014***(−0.86)(2.34)(−4.29)Business age: 11–20 years0.0070.014–0.021***(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 – 10.044***–0.044***0.001(5.92)(−6.89)(0.51)Locationt: Urban area0.031***–0.040***0.009**(4.50)(−9.69)(2.02)Family owned0.014–0.055***0.041***(1.22)(−3.60)(8.12)Business plan0.007–0.024***0.018***(1.25)(−2.92)(3.84)Female ledt – 10.101***–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)Fixed effectsRegional FEsYesIndustry FEsYesObservations3892Log likelihood–2443.91AIC4891.835BIC4904.368Note: 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.Impact of the COVID‐19 pandemic on future business plansThe 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 development 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, machinery, 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 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.6TABLESMEs with future plans over the next 3 years affected by COVID‐19 pandemic.Increase the skills of the workforceIncrease the leadership capability of managersCapital investment in United KingdomCapital investment in overseas marketDevelop and launch new products/servicesTraditional nonprofitt – 1−0.237***−0.1220.043n.e0.096**(−7.90)(−1.43)(0.68)(.)(2.30)Social enterpriset – 10.020−0.143***−0.314***0.586***−0.033*(0.87)(−4.95)(−30.00)(907.06)(−1.80)Socially oriented SMEt – 1−0.030*−0.107**−0.070*0.547***0.051(−1.92)(−1.99)(−1.70)(21.76)(0.99)Aims to growt – 1−0.064***0.042**0.140***−0.147***0.106**(−4.84)(1.97)(31.50)(−16.21)(2.19)Size: Micron.en.e0.081***0.017–0.112***(.)(.)(2.59)(0.90)(−5.48)Size: Small0.0140.0020.151***−0.097***−0.058*(0.62)(0.06)(2.66)(−30.95)(−1.69)Size: Medium0.106***0.127***0.143***0.064**−0.101(4.09)(3.99)(3.58)(2.22)(−1.30)Business age: 6–10 years−0.128***0.100***−0.114**0.531***−0.267(−27.26)(4.54)(−2.51)(49.75)(−1.45)Business age: 11–20 years−0.108***−0.089−0.102***0.575***−0.082(−3.43)(−1.15)(−10.74)(14.44)(−0.62)Business age: 20+ years−0.068***0.066−0.269***−0.124***−0.220**(−7.46)(1.58)(−5.61)(−6.81)(−2.48)Turnover change (stayed the same)t – 1–0.076*–0.090***–0.031***–0.387***–0.042(−1.66)(−2.95)(−3.90)(−19.01)(−1.26)Turnover change (increased)t – 1–0.085***–0.098***0.033–0.278***–0.033***(−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: Urban area0.002–0.0150.012–0.230***0.006(0.07)(−0.55)(0.18)(−3.11)(0.24)Family owned0.090***–0.0280.077***–0.431***0.017(8.51)(−1.17)(4.24)(−20.06)(0.72)Business plan0.052***–0.074***–0.002–0.090***0.137***(141.83)(−4.94)(−0.21)(−5.36)(41.32)Female ledt – 10.037*0.0470.1210.282***0.115***(1.92)(0.53)(0.95)(3.54)(5.78)Minority ethnic ledt – 10.141***0.151***0.005–0.863***–0.050**(15.26)(22.90)(0.28)(−32.28)(−2.36)Fixed effectsRegional FEsYesYesYesYesYesIndustry FEsYesYesYesYesYesObservations67245139958456Log pseudo‐likelihood−142.109−77.933−148.533−8.296−283.218R20.0430.0650.1660.6660.072AIC288.219159.866301.06718.591570.435BIC297.239168.089309.04520.652578.680Note: This table shows average marginal effects from probit regressions. 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. 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.7TABLESMEs with future plans over the next 3 years affected by COVID‐19 pandemic.Introduce new working practicesInvest in R&DIncrease export sales or begin selling to new overseas marketsRecruitment of new staff in the United KingdomRecruitment of new staff in overseas officesTraditional nonprofitt – 1−0.135−0.068***n.e0.038−0.013(−0.87)(−4.12)(.)(0.81)(−0.18)Social enterpriset – 1–0.186***0.186***0.535***0.042***–0.121**(−6.28)(6.60)(12.95)(4.09)(−2.46)Socially oriented SMEt – 10.0890.174***0.047*0.106***0.242**(1.48)(5.78)(1.70)(9.26)(2.43)Aims to growt – 10.102**0.0320.0030.066***–0.060(2.55)(0.92)(0.04)(5.36)(−0.56)Size: Micro–0.035–0.0400.070*–0.109***0.467***(−1.03)(−0.88)(1.87)(−6.86)(5.82)Size: Small–0.058–0.0320.078–0.148***0.236***(−0.51)(−0.85)(0.99)(−7.38)(2.99)Size: Medium0.074–0.006–0.018–0.077***0.336(0.64)(−0.09)(−0.26)(−2.89)(1.62)Business age: 6–10 years0.110–0.453***–0.239***–0.147***0.012(0.97)(−12.41)(−25.06)(−9.22)(0.20)Business age: 11–20 years0.092**–0.270***0.026–0.091***0.143***(2.46)(−35.09)(1.56)(−14.94)(3.22)Business age: 20+ years0.048***–0.259***–0.170***–0.138***–0.010(3.07)(−5.96)(−3.23)(−28.14)(−0.08)Turnover change (stayed the same)t – 10.003–0.111***–0.217***–0.110***–0.293***(0.06)(−36.09)(−145.77)(−9.91)(−39.07)Turnover change (increased)t – 1–0.078*–0.193***–0.232***–0.167***–0.206***(−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)Locationt: Urban area0.137***0.0450.052***–0.052***0.139*(3.10)(1.21)(49.96)(−8.16)(1.85)Family owned0.030***0.085***–0.028**0.072***–0.062**(3.39)(5.16)(−1.98)(8.33)(−2.22)Business plan0.0200.129***–0.090***0.138***–0.016***(0.70)(12.28)(−6.19)(17.39)(−2.84)Female ledt – 10.034–0.170***–0.053***–0.066***0.131***(0.23)(−15.32)(−8.56)(−3.94)(2.70)Minority ethnic ledt – 1–0.130**0.043***–0.182***–0.155***0.014**(−2.51)(3.22)(−15.97)(−23.41)(2.30)Fixed effectsRegional FEsYesYesYesYesYesIndustry FEsYesYesYesYesYesObservations51133320765866Log pseudo‐likelihood–243.464–108.114–91.832–220.690–7.739R20.1530.2280.2050.0720.649AIC490.928220.228187.665445.38117.479BIC499.401227.844194.330454.35919.668Note: This table shows average marginal effects from probit regressions. 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. 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.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 enterprises, 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 investment 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.Access and use of COVID‐19 fundingFinally, 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 Interruption 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 subsidies, streamlined administrative processes for accessing specific type of funding, or other forms of financial assistance. 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.8TABLEUse 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 Back LoanCoronavirus COVID‐19 business grants funded by government or local authorityModel 1Model 2Model 3Model 4Traditional nonprofitt – 1–0.188***–0.204***–0.079–0.053***(−37.56)(−26.04)(−1.23)(−5.97)Social enterpriset – 1–0.044**–0.041**0.021**0.026(−2.51)(−2.19)(2.25)(1.59)Socially oriented SMEt – 1–0.007–0.0060.019**0.006***(−0.77)(−0.68)(2.46)(4.56)Aims to growt – 10.124***0.126***0.093***0.055***(26.63)(34.62)(6.09)(3.26)Size: Micro0.153***0.151***0.174***0.209***(4.97)(4.65)(293.09)(23.54)Size: Small0.181***0.169***0.117***0.142***(5.20)(4.82)(7.94)(10.66)Size: Medium0.131***0.120***–0.039***–0.003(5.80)(5.60)(−2.99)(−0.59)Business age: 6–10 years0.0100.026–0.0600.003(0.65)(1.44)(−1.61)(0.35)Business age: 11–20 years–0.0110.003–0.032–0.012(−1.21)(0.35)(−0.78)(−0.97)Business age: 20+ years–0.075***–0.057**–0.022–0.027***(−4.00)(−2.51)(−0.43)(−6.56)Turnover change (stayed the same)t – 10.0040.008–0.053***0.015(0.30)(0.54)(−2.88)(0.54)Turnover change (increased)t – 1–0.012–0.006–0.049–0.006(−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 area–0.003–0.008–0.0210.010(−0.20)(−0.48)(−1.40)(0.62)Family owned0.0380.021–0.049***0.052***(1.58)(0.69)(−4.17)(12.58)Business plan0.026***0.031***–0.0050.014*(4.45)(4.49)(−0.76)(1.70)Female ledt – 1–0.054***0.025***(−4.67)(2.74)Minority ethnic ledt – 10.058***0.034***(2.79)(7.17)Fixed effectsRegional FEsYesYesYesYesIndustry FEsYesYesYesYesObservations4229387042273868Log pseudo‐likelihood–2134.441–2018.242–2405.890–2332.768R20.0810.0820.0780.089AIC4272.8824040.4854815.7804669.536BIC4285.5814053.0074828.4794682.057Note: 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.CONCLUSIONSThe COVID‐19 pandemic has led to unprecedented challenges for TSOs (nonprofit organizations and social enterprises) 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 pandemic, 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 evaluate the impact of lockdown measures implemented in the United Kingdom, exploring whether they have negatively 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 funding 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 obstacle 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 practices 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 oriented 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 findings 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 alternative 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 measures 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 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 organizations 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.ENDNOTES1TSOs encompass a diverse range of organizations such as registered charities, cooperatives, social enterprises, or companies 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 1.2In February 2023, the Department of Business, Energy and Industrial Strategy was divided into three constituent departments, comprising: the Department for Business and Trade; the Department for Energy Security and Net Zero; and the Department for Science, Innovation and Technology.3Resilience (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.4Some (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 indicate 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.’ACKNOWLEDGMENTSWe 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 Markets 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 STATEMENTThe 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.REFERENCESBacq, 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. 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Journal

Financial Accountability and Management – Wiley

Published: Aug 1, 2024

Keywords: COVID‐19 pandemic; organizational resilience; small‐ and medium‐sized enterprises; social enterprises; third sector

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