Fan Li et al., Identifying effective measures to enhance the recovery of small and micro enterprises after multiple waves of COVID-19 – A case study from Dongguan, China (2024)
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Article by Fan Li, Matteo Rubinato and Tao Zhou in the International Journal of Disaster Risk Reduction, vol. 106 (2024), article 104427, on measures that helped small and micro enterprises in Dongguan, China recover after multiple waves of COVID-19. Using two field surveys of 592 enterprises over two years and a spatial probit model, the authors find that recovery performance showed a positive spatial correlation that varied by local region. Self-media marketing, borrowing from family or friends, requesting bank loans and tax relief were the key measures that helped businesses stay open. Their effect varied with owner age and experience, primary market, business size and pre-pandemic finances, and the authors recommend differentiated assistance policies by business type and region.
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Identifying effective measures to
enhance the recovery of small and micro
enterprises after multiple waves of
COVID-19 – A case study from Dongguan,
China
Li, F., Rubinato, M. & Zhou, T
Published PDF deposited in Coventry University’s Repository
Original citation:
Li, F, Rubinato, M & Zhou, T 2024, 'Identifying effective measures to enhance the
recovery of small and micro enterprises after multiple waves of COVID-19 – A case
study from Dongguan, China', International Journal of Disaster Risk Reduction, vol.
106, 104427. https://doi.org/10.1016/j.ijdrr.2024.104427
DOI 10.1016/j.ijdrr.2024.104427
ISSN 2212-4209
Publisher: Elsevier
© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under
the CC BY license (http://creativecommons.org/licenses/by/4.0/).
International Journal of Disaster Risk Reduction 106 (2024) 104427
Contents lists available at ScienceDirect
International Journal of Disaster Risk Reduction
journal homepage: www.elsevier.com/locate/ijdrr
Identifying effective measures to enhance the recovery of small
and micro enterprises after multiple waves of COVID-19 – A case
study from Dongguan, China
Fan Li a, b, **, Matteo Rubinato c, *, Tao Zhou d
a
School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan, 523808, China
b
Guangdong Provincial Key Laboratory of Intelligent Disaster Prevention and Emergency Technologies for Urban Lifeline Engineering, Dongguan
University of Technology, Dongguan, 523808, China
c
Centre for Agroecology, Water and Resilience, Coventry University, Ryton Gardens, Wolston Lane, Ryton-on-Dunsmore, CV8 3LG, UK
d
School of Management Science and Real Estate, Chongqing University, Chongqing, 400044, China
A R T I C L E I N F O A B S T R A C T
Keywords: The ability of a small and micro enterprise (SME) to recovery after a pandemic outbreak can
Business recovery directly affect national economic growth and sustainable development (Sustainable Development
Multiple waves of pandemics Goal 11). Thus, the current study quantitatively identifies, based on the spatial probit model,
Spatial correlation effective measures that enhance the performance and survival of SMEs after multiple waves of
Spatial probit model shocks induced by COVID-19. Additionally, this work explores the spatial correlation for post-
SMEs
pandemic recovery performance of businesses adopting a novel approach. Two field in
vestigations were conducted in Dongguan City, in China, where questionnaires were released to
592 SMEs over a two-year period. The results obtained showed that the overall recovery per
formance of SMEs after the pandemic showed a positive spatial correlation, while this correlation
varied at different local regions. More in detail, self-media marketing, borrowing money from
family or friends, requesting bank loans and tax relief have been identified to be the key measures
to effectively support the recovery and the increase chances for small businesses to remain
operative after the pandemic. However, the success of these measures varies across businesses
with different characteristics (owner’s age and industry experience, primary market and business
size, pre-pandemic financial condition). It is then suggested based on the results obtained that
policy managers should formulate differentiated policies in terms of assistance measures for
businesses targeting dissimilar characteristics as well as the needs of different regions, because
those may have been impacted differently by the pandemic.
1. Introduction
Worldwide business operations can daily be exposed to potential disruptions that range from various types of uncertain emer
gencies, for example disasters, production accidents and public health events [1–3]. The novel coronavirus pneumonia outbreak in late
2019 (COVID-19) was the fastest spreading and most widespread public health emergency since the founding of New China (1949) [4],
[5], [6]. In late 2022, the full liberalization of the pandemic-lockdowns sanctioned China’s basic environment from the phase of
* Corresponding author.
** Corresponding author. School of Environment and Civil Engineering, Dongguan University of Technology, Dongguan, 523808, China.
E-mail addresses: fanli@dgut.edu.cn (F. Li), matteo.rubinato@coventry.ac.uk (M. Rubinato).
https://doi.org/10.1016/j.ijdrr.2024.104427
Received 13 December 2023; Received in revised form 19 March 2024; Accepted 19 March 2024
Available online 22 March 2024
2212-4209/© 2024 The Authors. Published by Elsevier Ltd. This is an open access article under the CC BY license
(http://creativecommons.org/licenses/by/4.0/).
F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
normalized pandemic prevention to the post pandemic era. However, it is fundamental to stress that the prolonged shock caused by the
pandemic between 2019 and 2022 has had a serious impact on the survival and development of small enterprises, leading to low
operating profits, high vulnerability, and short life cycles, aspects which nowadays seriously threaten economic and social stability and
sustainable development [7–12].
Small businesses for retail industry in China have between $147,800- $738,800 of revenue, and between 10 and 50 employees,
while micro businesses have less than $147,800 revenue and less than 10 employees. On the other hand, medium and large businesses
for retail industry in China have between $738,000- $29,520,000 of revenue, and between 50 and 300 employees for the first category,
and more than $29,520,000 of revenue, and more than 300 employees for the latter. Thus, small and micro businesses (SMEs) may be
more vulnerable to crisis and have a harder time recovering from it than medium/large businesses because the formers usually have a
high risk of revenue loss [13], poor financial condition [14], weak disaster planning [1], limited technological skills [15], and limited
support resources [16]. These characteristics are crucial and can then lead to significant differences in the crisis response or in the
recovery paths between SMEs and medium/large businesses. For example, the weak disaster planning of SMEs may easily lead them to
be overwhelmed after an outbreak and take highly randomized coping measures without a specific plan [17]. Moreover, the high
vulnerability may lead to a greater amount of workload faced by SMEs to cope with a crisis [13] and no relevant funding to cover those
tasks. Finally, SMEs have limited access to external support resources, and they may tend to get help from friends, relatives, or family to
cope with a crisis [18], thus generating other debts to close members. According to statistics, SMEs accounts for more than 80% of the
total number of Chinese enterprises and contributes more than 60% to GDP [19]. Therefore, the resilience and ability to recover of
SMEs after external shocks such as COVID-19 global pandemic are particularly predominant and imperative.
The performance of SMEs after the pandemic shock has been the goal of several studies. More in detail, some have explored the
differences in retail sales before and after the pandemic [20–23], the differences in income of small leisure and hospitality sectors [10,
24,25] and the differences in consumer mindset [10,26–29], as well as spatial differences in firm vulnerability during the pandemic
[30] from a macro perspective. On the other hand, additional studies have been conducted from a micro point of view to gain an
in-depth understanding of each enterprise’s operation through literature review, questionnaires, interviews, etc. [31–33]. Most of
them focus on two aspects: the first one is the impact of the pandemic on the enterprise [13,16,34–36], while the second one regards
the measures taken by each enterprise to cope with the pandemic [37–39]. However, most of the abovementioned studies are based on
questionnaire or interview data qualitatively describing the enterprises’ performance in the context of the pandemic and lack
quantitative tests of the effectiveness of firms’ responses to the pandemic on their recovery outcomes.
Finally, a few studies have explored the factors influencing the recovery of firms after the shocks caused by the pandemic through
quantitative methods [40–42]; Most of them focused on the performance of firms after a particular epidemic shock, however, lack
insights into their recovery following multiple waves of the pandemic and the consequent lockdowns targeting the long-term
perspective. Furthermore, the abovementioned studies also ignore the influence of spatial correlation factors for example the
geographical location of an enterprise, which has been demonstrated to affect recovery of a business after external shocks [43–45].
Thus, adding the geospatial factors into the modeling would be hugely beneficial in enriching the results obtained to date.
To address this gap, the current study takes Dongguan City, in China, as an example, exploring the spatial correlation of post-
pandemic recovery performance of SMEs and constructing a spatial probit model to test the effectiveness of enterprise response
measures on their recovery outcomes following multiple pandemic waves. Data was obtained through a 2-year questionnaire in
terviews and field investigations on 592 owners of SMEs in 23 districts of Dongguan City. Theoretically, the current study provides a
research of businesses recovery after a prolonged (multi-wave) pandemic shock, which helps to enrich existing theories of enterprise
risk-management that have been developed so far but focus on short-period shocks. Practically, the current study is dedicated to
identifying effective measures to promote enterprise recovery after long-term pandemic shocks, to accumulate experience related to
enterprise crisis management in the context of any pandemic. Moreover, this study aims to contribute to three essential practical goals,
which refer to i) improve the resilience and recovery capacity of small and micro enterprises after potential risk shocks in the future; ii)
enhance the ability of enterprise operators and policy managers to cope with external shocks, and iii) promote economic and social
stability and sustainable development.
2. Business measures to cope with the COVID-19 pandemic
Existing studies have summarized many measures that may affect the recovery of businesses after the pandemic, which can be
roughly divided into two categories: the internal adjustment measures and the external resources obtained by businesses to cope with
the impacts of the pandemic. The first category mainly refers to the behavior of businesses to adjust their internal personnel, products,
and technologies in response to the pandemic, involving actions such as layoffs, technological reforms, online transformation, opening
branches, and self-media marketing. Abu Hasan et al. (2022) and Siuta-Tokarska [46] conducted extensive literature and implemented
questionnaires and were able to summarize the main measures for SMEs to cope with the pandemic, including reduction of employees,
online transformation or digitization, technological innovation and training, and opening branches in areas less affected by the
pandemic [33,46]. Digital technology and online transformation were the most widely used strategies by SMEs during the pandemic.
Indeed, Drydakis [47] and Gao et al. [42] found that Artificial Intelligence (AI) and digital marketing strategies can improve the
productivity and financial performance of SMEs, reduce business risks and improve the sustained performance of the business during
the pandemic [42,47]. There is evidence that planning to combat a crisis has a significant impact on business resilience [1,48].
However, Katare et al. [40] found that not all transformation measures can help improve the survival outcomes of a business. For
example, adopting an online transformation may not work initially, nevertheless increasing a business’s influence on social media
could reduce revenue loss and recovery time [40].
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F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
The external resources obtained by any enterprise in response to the pandemic can be categorized into three main types: i) bonding
resources, ii) bridging resources, and iii) indirect resources [49]. The first ones refer to resources that are strongly related to the
enterprise and aid its initial recovery, for example the help provided by family or friends. The second category incorporate resources
that are weakly related to the businesses and promote them to obtain more help, for example supplier assistance. The last group is
associated with resources that do not have a direct relationship with the enterprise, but can provide convenience for the businesses,
such as governmental enable policies [50]. Many studies have pointed out that SMEs usually have worse recovery outcomes from
external crisis than large firms because the former have less access to external assistance resources than the latter [15,18]. Fauzi et al.
[37] and Katare et al. [40] believe that government assistance measures (such as subsidies, tax relief, rent relief, etc.) are conducive to
increasing business resilience and helping business survive the pandemic [37,40]. Nevertheless, the effect of the governments’
assistance on improving businesses’ resilience may vary across industries and businesses of different sizes [51]. SMEs with limited
access to government or institutional support resources may be inclined to seek help from relatives or family to cope with the crisis
[18].
The abovementioned research found in literature provide an important reference for the current study to investigate the key
measures that affect the recovery outcomes of businesses following a pandemic. However, most of the previous studies found in
literature summarize the measures adopted by businesses qualitatively through literature reviews or questionnaire surveys, and
therefore lack further quantitative testing of the effectiveness of these measures in promoting the outcomes of the recovery for each
business after the COVID-19 crisis. Moreover, spatial factors are considered to have a significant impact on the recovery outcomes of
businesses after an external crisis, and most of the existing studies have neglected them in their modeling [44]. In addition, most
existing studies focus on the business performance after the pandemic in early 2020 and lack attention to the scenarios of recovery of
businesses after the impact of multiple waves of pandemics. For example, China’s pandemic control lasted for three years (from the end
of 2019 to the end of 2022), which provided a rare opportunity to study long-term external shocks compared to most disasters or
accidents that occur in a short period (such as typhoons, floods, earthquakes, fires, explosions, etc.). Comprehensively, considering
spatial factors in modeling and identifying quantitatively effective measures affecting the recovery outcomes of businesses after
long-term COVID-19 crisis is of great significance for guiding business owners and managers to effectively cope with long-term crisis
and thus promote economic and social stability.
In addition to all the measures described, there are also other factors that may affect the recovery of businesses after the pandemic,
for example, owner characteristics and businesses characteristics [52–56]. Pongtanalert and Assarut [49] argued that entrepreneurial
mentality affects businesses’ adaptability after a pandemic shock [49]. Helgeson et al. [57] proclaimed that female-owned firms are
more severely affected by pandemics [57] and that male-owned businesses have better survival outcomes after pandemic shocks [58].
Businesses owned by highly educated or older experienced owners were expected to achieve better recovery performance after an
external shock than their counterparts [43,58,59].
Additionally, business characteristics may also influence their recovery outcomes after the pandemic. Businesses in different in
dustries, sizes and financial conditions encountered different problems during the pandemic, which can all affect the recovery of
businesses affected by pandemic shocks [58]. Most studies found that large businesses are able to get access to more resources to help
them recover, and thus, they recover faster than the SMEs [13,16]. Leased businesses still must pay rent despite low revenues during
the pandemic period and may face worse financial conditions compared to owned ones [60]. Good financial conditions before the
pandemic shock can help firms to keep their capital chain intact during the COVID-19 crisis, thus promoting the continued operation of
the businesses [40].
It is worth noting that the concept of recovery is not uniform across the world because some previous works define recovery as the
return of a system to a normal state or to its pre-crisis state [61–63], while other studies believe that recovery is a subjective concept
and that whether a system recovers or not depends on people’s subjective judgment [64]. Furthermore, a relatively widely accepted
concept states that recovery is the ability of a system to recover and rebuild quickly from a shock [65–67] and a number of studies have
defined the recovery ability of a system as its resilience, specifically adaptation resilience [68–70]. Distinguished from internal
resilience, which emphasizes the ability of a system to cope with potential disasters, adaptation resilience accentuates the ability of a
system to respond and recover from a crisis [71,72]. Therefore, there is a cross-correlation or overlap between the concepts of recovery
and resilience, and both can reflect a system’s ability to cope with environmental change [56,73]. However, both the concepts of
recovery and resilience have gradually shifted from emphasizing the return of the system to pre-crisis levels to the adaptation of the
system to the new post-crisis environment [54,74,75]. Clarifying the concept of recovery is fundamental to measure the recovery rate
of businesses. Existing research on this topic mainly measures business recovery performance in terms of whether a business reopens
after a crisis, or measures the speed of recovery in terms of the length of time it takes for a business to reopen after a crisis [40,44,59].
The field of business resilience mainly use financial indicators, physical indicators and spatial indicators, or a combination of multiple
aspects, with financial indicators and supply chain indicators being the relatively most important indicators of post-pandemic business
resilience concerns [76–79]. Based on this, the current study combines previous research and considers the accessibility of various
indicators and measures the recovery of businesses after the pandemic shock in terms of the survival status (open or closed) and
operational status (changes in the level of average profitability).
The current study aims to contribute to this field by identifying effective measures that can affect the recovery outcomes of
businesses after multiple waves of pandemic crisis, which is lacking within existing studies. The main goal focuses on improving the
accuracy of numerical modelling predictions by incorporating factors in Table 1. Some indicators had been previously mentioned, but
only qualitatively, however the effectiveness of these indicators had not been tested quantitatively. Furthermore, others were studied
but not in the context of global pandemic. Accordingly, the current study aims to address the following two research questions.
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Table 1
Summary of factors considered for the modeling of businesses’ survival dealing with shocks induced by a pandemic.
Categories Indexes References
Measures to cope with the pandemic Internal adjustment measures Layoffs [33,46]
Technology reform and innovation [42,47]
Online transformation [40]
Opening branches [31,46]
Purchase of anti-pandemic materials [1,48]
Self-media marketing [40]
External resources Borrow money from family or friends [18]
Bank loans [15,18]
Tax relief [37,50]
Rent relief [37,40]
Advance goods from suppliers [49]
Additional factors Owner characteristics Age [58,59]
Gender [57,58]
Education [43,59]
Industry experience [43,58]
Owner’ confidence in the Business climate [49]
Business characteristics Type of business [58]
Owned or leased facility [60]
Primary market [58]
Business size [13,16]
Pre-pandemic financial condition [40]
Spatial factor The distance between businesses [44]
(1) What are the spatial correlations of recovery performance of SMEs after multiple waves of pandemics?
(2) What response measures are effective in facilitating SMEs recovery after multiple pandemic waves considering spatial
correlations?
3. Methods
3.1. COVID-19 pandemic and the study area
Dongguan is in the South of China, more specifically in the south-central part of the Guangdong Province, and it is adjacent to two
first-tier cities, Shenzhen, and Guangzhou, to the north and south respectively, as shown in Fig. 1. By the end of 2022, Dongguan had
10.437 million permanent residents, of which 9.628 million were urbanized, with an urbanization rate of 92.25%. Dongguan has a
total area of 2542.67 square kilometers, with 4 central urban districts and 28 towns under its jurisdiction. Unfortunately, the high
urbanization rate, the dense urban population, and the close connection with other cities were all prerequisites that facilitated a rapid
spread of COVID-19 pandemic [71,80–82].
As it also happened for many other cities in China, Dongguan has been fighting the pandemic for three years (end of 2019 - end of
2022). The initial outbreak at the end of 2019 was mainly concentrated in Wuhan, China, and at this time of the year there is the Spring
Festival which unfortunately enabled a more intense spread of the pandemic through China due to people returning from this event to
their homes [83–85]. Between the end of 2019 and June 2021, Dongguan was mainly dealing with confirmed COVID-19 cases coming
from Wuhan. Timely screening and isolation of imported cases from Wuhan resulted in little transmission between these infected
persons and Dongguan natives, and as a result, production and life in Dongguan was relatively normal. The government did not take
wide-scale lockdown measures, and the impact of the pandemic on businesses was mild during this period. However, in other regions
such as Wuhan itself, consequences of the pandemic were severe. From June 2021 to December 2022, the pandemic began to spread
wantonly in local Dongguan, and then Dongguan’s government had to take a certain range of lockdown measures, resulting in a large
number of businesses being seriously affected by the pandemic. The city experienced a total of five waves of pandemics, with the
specific development timeline shown in Fig. 2. It should be noted that, according to the data published by the Dongguan Municipal
Health Bureau, the current study takes into consideration the fact that if both confirmed and new cases recorded are 0 for more than 14
days, this corresponds to the end of a certain wave of the pandemic [86]. Among the five waves of the pandemic, two of them
(2022.6.1–2022.6.10 and 2022.7.7–2022.7.16) had only 1 new confirmed case, indicating that the pandemic was relatively mild,
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F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
Fig. 1. Study area and geographical distribution of the samples.
Fig. 2. Timeline of the field investigation conducted and of the development of the pandemic in Dongguan.
while the remaining three-time phases (2021.6.18–2021.7.15, 2021.8.24–2022.4.26, and 2022.8.21–2022.12) faced more severe
effect and infected people.
3.2. Data collection and methods applied
3.2.1. Sampling procedure
The current study is devoted to identifying additional effective measures that may influence the rate of recovery and the perfor
mance of businesses affected by multiple waves of pandemic. The datasets were obtained from two field investigations and ques
tionnaire surveys of 592 SMEs in 4 central urban districts (Dongcheng Street，Nancheng Street，Guancheng Street, Wangjiang Street)
and 15 towns in Dongguan, affected by relatively severe infected people. The first field investigation and questionnaire survey were
conducted in July 2021 and the second in July 2023, two years later. The former was used to understand the response measures, the
owner’s characteristics, and the businesses’ characteristics during the pandemic period; the latter, six months after the full liber
alization from the pandemic’s lockdown, was used to understand the response measures and the performance of businesses to recover
after the pandemic.
Other 13 towns in Dongguan, including Qishi, Qiaotou, Dongkeng, Zhangmutou, Xiegang, Tangxia, Qingxi, Fenggang, Mayong,
Zhongtang, Gaobu, Shijie and Hongmei, were considered to have very few confirmed cases during the outbreak period, and
Table 2
Distribution of valid business samples collected.
Name of town Number of valid samples Name of town Number of valid samples
Chashan Town 20 Humen Town 41
Changping Town 40 Liaobu Town 26
Dalang Town 46 Nancheng Street 22
Dalingshan Town 31 Shatian Twon 38
Daojiao Town 29 Shilong Town 39
Dongcheng Street 20 Shipai Town 41
Guancheng Street 20 Wangniudun Town 29
Hengli Town 40 Wangjiang Street 20
Houjie Town 31 Chang’an Town 39
Huangjiang Town 20 Total 592
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F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
experienced mild outbreak shocks, thus, these towns were excluded from the study. For the remaining 15 towns and 4 central urban
districts considered, the relatively busiest and most densely populated areas of the city center with the highest concentration of SMEs
were selected. Basic information such as the name, location, and business type of the SMEs were observed and recorded, and business
owners were asked to do a questionnaire survey to get a comprehensive understanding of the response measures implemented and the
survival status of the SMEs after the pandemic. After excluding samples owned by those who are unwilling to participate in the survey
or those whose questionnaires were incomplete, the number of valid business samples for each town or central urban district is shown
in Table 2 and the spatial distribution of these business samples is shown in Fig. 2.
3.2.2. Node variables
The current study identifies effective measures affecting the recovery performance of SMEs after multiple waves of pandemic crisis
based on a spatial probit model. The selected dependent variable is the recovery performance of the businesses, the independent
variable is the response measures of the businesses to cope with the pandemic, and the control variables are the characteristics of the
owner characteristics and the characteristics of the businesses. The recovery performance of SMEs is grouped into four categories (see
Table 3): SMEs can perform better, or the same way, or worse after the pandemic (2023.1–2023.7) than they did before the pandemic
(before 2020.1), denoted by 3, 2, and 1, respectively. Poor recovery performance that leads to the closure of a business is denoted by 0.
A business was defined as closed if it had been not opened for more than three months at the time of the field investigation, and this
judgment was confirmed by inquiring surrounding businesses or locals living in the area.
The response measures for SMEs include 6 internal adjustment measures and 5 external assistance measures, calculated dichot
omously: i) whether the business has taken measures of layoffs (or technology reform and innovation, or online transformation, or
opening branches, or purchase of anti-epidemic materials, or self-media marketing); ii) whether the business borrowed money from
family or friends (or received bank loans, or received tax relief, or received rent relief, or advanced goods from suppliers). These
measures are taken specifically in response to the pandemic and do not count if they were used before the COVID-19 pandemic.
The control variables include 5 variables describing the owner characteristics and 5 variables describing the businesses charac
teristics. Gender, owned or leased facility are as well both measured dichotomously. Business types are divided into four categories:
manufacturing, wholesale/retail, service, others. Among them, manufacturing mainly includes food processing, textile manufacturing,
furniture manufacturing; wholesale/retail mainly includes daily necessities, clothing, building materials, books and stationery, digital
products, automobile accessories; service mainly includes restaurants, beauty salons, hotels, agencies, bars; and others mainly includes
software development, advertisement design. Considering that dividing business types into more than a dozen categories may lead to
bias in the results of the regression model, the business types were therefore grouped into these four categories. The other variables,
owner’s age, education, industry experience, confidence in the business climate, primary market, business size, pre-pandemic financial
condition, are all measured on ordinal scale. Of these, “confidence in the business climate” is measured by asking the owner “How do
Table 3
Index and coding scheme selected for the variables involved in this study.
Indexes Coding scheme
Business recovery performance 0 = closed, 1 = performance is worse after the pandemic than before the pandemic, 2 = performance after the pandemic was
like that before the pandemic, 3 = performance is better after the pandemic than before the pandemic
Business response measures Whether the business has taken the following COVID-19 response measures?
Layoffs 1 = yes, 0 = no
Technology reform and innovation 1 = yes, 0 = no
Online transformation 1 = yes, 0 = no
Opening branches 1 = yes, 0 = no
Purchase of anti-pandemic 1 = yes, 0 = no
materials
Self-media marketing 1 = yes, 0 = no
Borrow money from family or 1 = yes, 0 = no
friends
Bank loans 1 = yes, 0 = no
Tax relief 1 = yes, 0 = no
Rent relief 1 = yes, 0 = no
Advance goods from suppliers 1 = yes, 0 = no
Control variables Owner characteristics and business characteristics
Age 1 = 20 or below, 2 = 21–35, 3 = 36–50, 4 = 51 and above
Gender 1 = male, 2 = female
Education 1 = primary school or below, 2 = junior middle school, 3 = high school/junior college, 4 = university degree or above
Industry experience 1 = less than half a year’ experience, 2 = 0.5–2years’ experience, 3 = 2–3.5years’ experience, 4 = 3.5–5years’ experience, 5
= more than 5 years’ experience
Owner’ confidence in the business 1 = extremely confident, 2 = relatively confident, 3 = moderate, 4 = little confident, 5 = no confident
climate
Type of business 1 = manufacturing, 2 = wholesale/retail, 3 = service, 4 = others
Owned or leased facility 1 = owned facility, 2 = leased facility
Primary market 1 = national, 2 = provincial, 3 = municipal, 4 = town, 5 = local
Business size 1 = 1 full-time employees, 2 = 2–4 full-time employees, 3 = 4–10 full-time employees, 4 = 10–50 full-time employees
Pre-pandemic financial condition 1 = extremely good, 2 = relatively good, 3 = moderate, 4 = relatively bad, 5 = extremely bad
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you think the business climate will be in the future compared to the present?” The answers “Much better; A little better; About the
same; A little worse; Much worse” indicate “Extremely confident; Relatively confident; Moderate; Little confident; No confident”,
respectively (see Appendix 1).
3.2.3. Statistical method
(1) Spatial probit model
The ordered probit model is applicable to ordered categorical variables, i.e., the dependent variable has multiple possible values,
and these values have a certain order relationship. This model was adopted because the dependent variable of the current study, post-
pandemic recovery performance, is an ordered categorical variable, and most of the previous studies have used ordered probit model in
solving the related problems [44,87]. It indicates that the use of ordered probit model to solve the question of the current study has a
certain assumption and its expression is as follows:
Y = βX + ε (1)
Where，β is the coefficient matrix, X is the dependent variable matrix, ε is the error term.
If there is a spatial correlation in the dependent variable, it is necessary to introduce a spatial matrix W, forming a spatial probit
model based on the ordered probit model. The expression is as follows:
Y = βX + ρWY + ε (4)
⎡ ⎤
1 1
⎢ 0 ⋯ ⎥
⎢ d12 d1n ⎥
⎢ ⎥
⎢ ⎥
⎢ 1 1 ⎥
⎢ 0 ⋯ ⎥
W =⎢
⎢ d21 d2n ⎥
⎥ (5)
⎢ ⎥
⎢ ⋯ ⋯ ⋯ ⋯ ⎥
⎢ ⎥
⎢ 1 1 ⎥
⎣ ⋯ 0 ⎦
dn1 dn2
Where, ρ is a spatial autocorrelation parameter, and W is a spatial weight matrix of n × n, which is used to represent the spatial position
relationship between n samples. Based on the law that the spatial weight correlation decreases with the increase of distance [88], the
inverse of the distance between sample points is used to represent the strength of its spatial correlation, and dij represents the distance
between sample point i and j. It is worth noting that in spatial measurement, distance means that it can denote geographic proximity or
Euclidean distance, but also the proximity of a cooperative relationship in the economic sense, or even the closeness of an interpersonal
relationship in the sociological sense [89]. Considering that the current study mainly focuses on the recovery of SMEs in different
geographic locations after the pandemic, thus, distance refers to physical distance.
(2) Global Moran’s I
The Moran’s I, a widely used index to describe spatial correlation, was used in the current study to explore the spatial correlation of
variables. The Moran’s I index includes global Moran’s I, which indicates the spatial correlation of a variable for the whole region; and
local Moran’s I, which indicates the spatial correlation of a variable for different local areas in this region [44]. The global Moran’s I is
calculated by the following formula:
∑ n
n ∑ ( )
wij (yi − y) yj − y
n i=1 j=1
I= × ∑n (2)
S0
(yi − y)2
i=1
∑
∑ n
n
S0 = wij (3)
i=1 j=1
Where, I is the value of Moran’s I, n is the number of samples, yi and yj denote the attribute values of the ith and jth samples,
respectively, ȳ is the mean of the attribute values of all the samples, and wij is the value of the spatial weights. The value of global
Moran’s I is in the range of − 1 to 1. If it exceeds 0, it denotes a positive autocorrelation, while if its value is less than 0 it indicates a
negative autocorrelation, and if it is close to 0 it indicates no spatial autocorrelation.
(3) Local Moran’s I and LISA cluster map
In order to further understand the local spatial correlation of variables and the significance of their correlations, the local Moran’s I
was calculated and the corresponding LISA cluster map was generated. The map usually includes the following four spatial correlation
forms: (1) HH, the region characterized by high values and high values clustered, indicating that the attribute values of a variable are
high in this region and also high in its surrounding areas, revealing a significant positive spatial autocorrelation for this variable in this
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F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
region; (2) HL, the region characterized by high values and low values clustered, indicating that the attribute values of a variable are
high in this region but low in its surrounding areas, revealing a significant negative spatial autocorrelation for this variable in this
region; (3) LH, the region characterized by low values and high values clustered, indicates that the attribute values of a variable are low
in this region but high in its surrounding areas, revealing a significant negative spatial autocorrelation for this variable in this region;
(4) LL, the region characterized by low values and low values clustered, indicating that the attribute values of a variable are low in this
region and also low in its surrounding areas, revealing a significant positive spatial autocorrelation for this variable in this region.
4. Results
4.1. Descriptive analysis
The distribution of the performance of SMEs that recovered after multiple waves of COVID-19 pandemic is shown in Fig. 3. Of the
592 SMEs, 115, or 19%, closed because of the pandemic shock; 196, or approximately one-third, reported worse performance after the
pandemic than that before the pandemic; 166, or 28%, reported approximate performance after the pandemic than that before the
pandemic; only 115, or 20%, reported better performance after the pandemic than that before the pandemic. Table 4 and Appendix 2
Fig. 3. Distribution of the recovery performance of SMEs after the pandemic.
Table 4
Statistics for variables adopted for this study and run with the probit model.
Indexes Mean St. dev. Max Min
Dependent variable
Business recovery performance 1.475 1.013 3 0
Independent variables
Layoffs 0.091 0.288 1 0
Technology reform and innovation 0.054 0.226 1 0
Online transformation 0.137 0.344 1 0
Opening branches 0.024 0.152 1 0
Purchase of anti-pandemic materials 0.956 0.205 1 0
Self-media marketing 0.083 0.276 1 0
Borrow money from family or friends 0.144 0.351 1 0
Bank loans 0.118 0.323 1 0
Tax relief 0.140 0.347 1 0
Rent relief 0.181 0.385 1 0
Advance goods from suppliers 0.052 0.223 1 0
Control variables
Age 2.726 0.639 4 1
Gender 1.517 0.499 2 1
Education 2.530 0.790 4 1
Industry experience 4.289 0.915 5 2
Owner’ confidence in the business climate 2.581 0.645 5 1
Type of business 2.493 0.615 4 1
Owned or leased facility 1.899 0.318 4 1
Primary market 3.895 1.087 5 1
Business size 2.056 1.069 4 1
Pre-pandemic financial condition 2.544 0.713 5 1
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show the descriptive results of the independent variables, dependent variables, and control variables in the spatial probit model. The
mean of recovery performance for SMEs after multiple waves of pandemic crisis is 1.475, which is between “performance is worse after
the pandemic than before the pandemic” and “performance after the pandemic was similar to that before the pandemic”. It indicates that the
performances of most SMEs have not recovered to the pre-pandemic level in the six months after the full liberalization from the
lockdowns induced by the pandemic.
Regarding the response measures taken by the SMEs to cope with the pandemic, the purchase of anti-pandemic materials (for
example, masks, disinfectants, hand sanitizers, etc.) is the most widely used measure, with a mean value of 0.956, indicating that,
95.6% of SMEs have adopted this measure. The next most widely used measure was the online transformation, with a mean value of
0.137. It was expected that many SMEs would adopt this technique during the pandemic, but since some SMEs already had an online
service model in place before the pandemic, only 13.7% of them implemented it after the pandemic. Few SMEs adopted measures such
as layoffs, technology reform and innovation, and opening branches, accounting for only 9.1%, 5.4% and 2.4% respectively. Several
SMEs received help from the following external sources: 14.4% borrowed money from family or friends, 11.8% asked for bank loans,
14% demanded tax relief and 18.1% sought rent relief, respectively. The relatively smallest number of SMEs, 5.2%, advanced goods
from suppliers.
In terms of the control variables, the mean value of gender is 1.517, indicating that the proportion of male and female owners
participating in the survey is relatively similar. The mean value of industry experience is 4.289, indicating that most of the owners have
more than three and a half years’ experience. The mean value of owners’ confidence in the business climate is 2.581, which is between
"relatively confident" and "moderate confident," indicating that most business owners’ confidence between partial to optimistic. The
mean values of the primary market and the business size are 3.895 and 2.056, respectively, indicating that most businesses serve the
township market, and have 2–4 employees. The mean value of pre-pandemic financial conditions is 2.544, between "relatively good"
and "moderate", indicating that the financial condition of most SMEs before the pandemic was partial to optimistic.
4.2. Results of Moran’s I index
The global Moran’s I index for the dependent variable ‘post-pandemic recovery performance’ was calculated and the results are
displayed in Fig. 4. It is shown that the value of Moran’s I index is 0.049, which is significant at the 1% level. It indicates that there is
spatial autocorrelation between the post-pandemic recovery performance of SMEs. That is, overall, the correlation between post-
pandemic recovery performance of SMEs became increase significant as their spatial locations clustered. Thus, the use of the
spatial probit model may improve the accuracy of the results.
The same method was used to calculate the global Moran’s I and its statistical test parameters for various pandemic response
measures taken by SMEs (see Table 5). It is shown that the global Moran’s I of all variables exceed 0, and the p-value are all below 0.05,
indicating that these variables all have a positive spatial autocorrelation and significant at the 95% level. Taking layoffs as an example,
this result indicates that there may be clustering in spatial distribution of SMEs that take layoffs measures. The majority of the global
Moran’s I for these variables range from 0 to 0.3 (close to 0), indicating a relatively weak spatial aggregation of response measures
taken by many SMEs in the study area.
The LISA cluster map of the post-pandemic recovery performance of SMEs is presented together with three response variables with
the relatively highest global Moran’s I (tax relief, borrow money from family or friends, online transformation) in order to show their
Fig. 4. Results of global Moran’s I index for post-pandemic business recovery performance.
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Table 5
Results of global Moran’s I index for pandemic response measures.
Index Global Moran’s I Z score P value
Layoffs 0.142 14.228 0.001
Technology reform and innovation 0.144 14.585 0.001
Online transformation 0.169 16.982 0.001
Opening branches 0.042 4.605 0.002
Purchase of anti-pandemic materials 0.156 14.884 0.001
Self-media marketing 0.054 5.708 0.003
Borrow money from family or friends 0.203 20.410 0.001
Bank loans 0.164 16.649 0.001
Tax relief 0.286 28.251 0.001
Rent relief 0.147 14.862 0.001
Advance goods from suppliers 0.148 15.894 0.001
Fig. 5. Results of LISA cluster map for several variables.
local spatial correlations (see Fig. 5). It is demonstrated that the spatial correlation of SMEs recovery performance after the pandemic
presents certain regional differences. The areas with extremely good recovery performance, charactered by high- and high-value
clustered, are mainly located in the northern part of the study area. The areas with extremely poor recovery performance (charac
tered by low- and low-value clustered) are mainly located in the middle part of the study area, which is close to the areas with good
recovery performance (charactered by high- and low-value clustered). The areas with poor recovery performance, charactered by low-
and high-value clustered, are scattered across various towns and even streets. It can be inferred that the spatial correlations of post-
pandemic recovery performance of SMEs not only exhibit differences at the macro level of the whole city, but may also vary among
different towns or even streets at the micro level.
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Table 6
Results of the spatial probit model.
Indexes (1) (2)
β Std. Error β Std. Error
Layoffs 0.216 0.361 0.447 0.637
Technology reform and innovation 0.429 0.632 0.316 0.863
Online transformation − 0.107 0.168 0.204 0.469
Opening branches 13.797 4.776 14.981 3.389
Purchase of anti-pandemic materials 0.0191 0.238 0.251 0.454
Self-media marketing 0.523 0.457 1.999*** 0.552
Borrow money from family or friends 0.151 0.259 0.655* 0.382
Bank loans 0.868* 0.559 1.502* 0.798
Tax relief 0.603* 0.419 1.936** 0.833
Rent relief − 0.234 0.145 0.504 0.362
Advance goods from suppliers − 0.089 0.360 0.023 0.834
Age 0.461** 0.194
Gender − 0.261 0.193
Education − 0.088 0.142
Industry experience 1.124*** 0.124
Owner’ confidence in the business climate − 0.136 0.209
Type of business 0.181 0.169
Owned or leased facility − 0.797 0.399
Primary market − 0.388*** 0.137
Business size 0.277** 0.118
Pre-pandemic financial condition − 0.645*** 0.167
Log-likelihood − 215.883 − 114.0079
R2 0.441 0.779
Note: Log-likelihood and R2 (coefficient of adjusted determination), are statistics that reflect the goodness of fit of the model; ***, p < 0.01; **, p < 0.05; *p < 0.1.
Models are calculated in Python.
Regarding the measures taken by SMEs, whether it’s tax relief, borrow money from family or friends, or online transformation, the
most frequent spatial association pattern is the cluster of low values and low values, indicating that many regions have not imple
mented these three pandemic response measures. Furthermore, these regions have a positive spatial autocorrelation, meaning that the
correlation of pandemic response measures becomes increase significant as the spatial distribution becomes more clustered. Secondly,
the proportion of regions with low- and high-value clustered is also large, indicating that these regions are relatively inactive in
implementing these pandemic response measures and exhibit negative spatial autocorrelation. In contrast, the regions that have
implemented response measures and formed high- and high-value clustered are relatively small, and the distribution of these clustered
areas across different measures exhibits significant spatial variation. Regions that have received tax relief, borrow money from family
or friends, and online transformation with positive spatial autocorrelation are primarily located in the north, south, and east of the
study area, respectively. This could be a potential reason for the spatial differences in post-pandemic recovery performance of SMEs
across different regions.
4.3. Results of the spatial probit model
Table 6 shows the results of two sets of spatial probit models, the first excludes the control variables (model 1) and the second
includes the control variables (model 2). It is essential to note that the use of the spatial probit model presupposes that there is no
multicollinearity between the variables. The results of the person-correlation test for the 21 independent and control variables show
that there is no significant correlation between any two variables, i.e., there is no multicollinearity. Therefore, it is appropriate to
include all the independent and control variables in the spatial probit model.
It is confirmed that when the control variables are excluded in the model, only bank loans and tax relief were identified as sig
nificant factors influencing businesses’ recovery performance after multiple waves of the pandemic. When the control variables are
included in the model, more variables become significant factors that influence post-pandemic recovery performance of the businesses,
such as self-media marketing, borrowing money from family and friends, bank loans, and tax relief. From the results of the model test
statistics, Log-likelihood and R2, it is shown that both the Log-likelihood and R2 of model 1 are smaller than those in model 2, which
indicates that model 1 fits the sample observations less well than model 2, and therefore, model 2 is better than model 1. Additionally,
it suggests that, on top of businesses’ responses to the pandemic, owner characteristics and business characteristics are also important
factors that affect their recovery after multiple waves of pandemic. Age, industry experience, primary market, business size, and pre-
pandemic financial conditions significantly affect businesses’ recovery performance after the pandemic. Therefore, a business needs to
consider the basic conditions of its owners and business when formulating pandemic response measures, and ignoring its own con
ditions, plus imitating the pandemic response measures adopted by other businesses may lead to failure.
5. Discussion
The spatial correlation of post-pandemic recovery performance of SMEs is not only varying across the macro city level, but also in
the micro town or even street level. Similarly, existing studies has also reminded that spatial correlations between businesses play a key
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F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
role in post-crisis business recovery [90]. There may be two possible reasons: on one hand, the distribution of SMEs clusters that
actively implement pandemic response measures exhibits significant spatial differences. Other relevant studies also emphasized that
the post-crisis recovery decisions of neighboring firms are highly interdependent, which easily leads to the emergence of many small
clusters with different strategies on the micro scale [91,92]. Spatial differences in pandemic response strategies may be a potential
cause for the spatial correlations of post-pandemic recovery performance of SMEs to vary across different regions. On the other hand,
other previous studies on businesses’ survival have mentioned that neighboring businesses that provide complementary or substitute
products or services can have a mutually promoting or inhibiting effect on each other’s survival [44,93], leading to differences in the
spatial correlation of recovery performances among SMEs in different streets or even on the same street that are close to each other.
Self-media marketing has been found to be the only factor that significantly affected the chances of recovery of businesses after the
pandemic. Many studies previously conducted have qualitatively described through questionnaires that many businesses usually adopt
an online business model after a pandemic [38,42,46], yet the online transformation has no significant impact on the survival rate of
businesses. This result is consistent with the findings of Katare et al. [40] for small businesses in the US. Similarly, it was found that
increasing the presence of a business on social media could reduce revenue loss and recovery time after a pandemic, but the online
transformation does not [40]. This may be because during the pandemic, the lockdowns made it vary inconvenient for many people to
socialize or play, and self-media became an important entertainment tool especially if incorporated within marketing since it could
expand the quality of products or services, leading to additional income and higher chances of survival. On the contrary, although
online transformation could provide courier services or an online management (thus no physical office required), the lack of effective
publicity could cause a limited spread of information related to the products or services delivered, thus failing to increase incomes or to
promote an efficient recovery after the pandemic. Moreover, the economic development suffered during the pandemic, and residents’
sources of income were reduced, with many tightening their spending and reducing the frequency of both online and offline con
sumption. As a result, online operations during the pandemic did not have a significant effect on increasing business revenues and
promote their recovery without vigorous promotion.
Borrowing money from family or friends has been found to facilitate the recovery of a business after multiple waves of pandemic.
Many studies have proposed that the breakdown of the financial chain or the reduction of revenue are the key factors that lead to
business’s closure after the pandemic [4,16,40]. Thus, borrowing money from family or friends could be the fastest and most timely
way to replenish funds compared to taking out a bank loan or applying for government subsidies [94], reducing the risk of closure due
to financial problems.
A positive correlation has been observed between taking a bank loan and the recovery performance of businesses after the
pandemic. A business’s borrowing capacity is an important indicator for its ability to cope with risk [11]. Min and Huang [95] sug
gested that financing for business operations is one of the most important strategies to facilitate the recovery of Taiwan’s tourism
industry after Severe Acute Respiratory Syndromes (SARS) in 2003 [95]. Although taking a bank loan may increase the financial
burden on firms [96], businesses that received bank loans during the pandemic typically had larger size or higher entrepreneurial
confidence [18], which could facilitate their recovery performance after the shocks and lockdowns induced by COVID-19 pandemic.
Businesses that received tax relief had better opportunities to avoid closure than those that were not given it. Other studies have
also reported a positive relationship between receiving tax relief and chances of survival after the pandemic [13,33,40]. Tax incentives
such as tax rebates, subsidies or tax rate reductions are one of the important measures the Chinese government has taken to help small
businesses cope with the pandemic [12]. Acquiring tax relief allows businesses to keep more funds to deal with the problems caused by
the pandemic (loss of income, high prices for material and resources etc.).
Owners with higher industrial experience had better opportunities to implement the right strategies to support their businesses and
their operation during difficult time. Other previous studies also suggest establishing repositories of knowledge obtained from previous
disruptions as an important measure to promote business resilience [45,62,97,98], due to its importance in helping owners to make
timely or correct crisis decisions [99].
SMEs that were holding a large number of employees or addressing large markets demonstrated to be facilitated in satisfying
various clients and therefore incrementing, or keep sustaining, their income. Most of the existing related studies have reported a
similar finding [1,15,100]. On the contrary, small businesses serving local markets were more vulnerable to crisis because they had
difficulty implementing recovery strategies in multiple locations [43]. Moreover, large businesses usually have access to more re
sources to facilitate them recover after the crisis [45,87].
Financial conditions before the pandemic arrived also had implications. There is evidence that businesses with affluent financial
conditions before the crisis had more to lose, leading them more vulnerable to the crisis [53]. However, business with good financial
conditions before the crisis may be capable of getting access to abundant external resources and have more funds to accommodate new
requests and limit losses [43,59] and these aspects can certain be in favor of survival and against closure.
6. Conclusions
The current study explores the spatial correlation of post-pandemic recovery performance of SMEs and identifies effective measures
based on spatial probit model that can facilitate the recovery and survival of SMEs after multiple waves of shocks induced by COVID-19
pandemic. Essential datasets were obtained through two field investigations and questionnaire surveys conducted within 592 SMEs in
Dongguan, China, during (July 2021) and after (July 2023) the pandemic. The current study enriches the findings of existing studies
that so far ignored spatial correlations and focused only on short-term single epidemic shocks or qualitative analysis.
This work has produced three main findings.
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Table 7
Summary of the findings and consequent implications.
Main findings Implications
For owners of SMEs For policymakers
The recovery performance of SMEs after the Owners need to consider the operate status of their Policymakers should formulate guidelines and policies
pandemic varies in spatial correlations neighboring SMEs when choosing the exact reopening which should vary according to businesses located in
among different local regions. time or the specific place to relocate after a pandemic different parts of the city as well as in different cities
shock. Moreover, these decisions should vary based on and regions.
the cluster area in which an owner holds its business.
Self-media marketing significantly affected The influence of SMEs’ products or services must be N/A
the recovery performance of SMEs after expanded via self-media using the entertainment plus
the pandemic. publicity model after the pandemic shock.
Borrowing money from family or friends Owners may consider borrowing money from family or N/A
could facilitate the recovery of a friends to solve the pandemic-induced financial
business after multiple waves of problems and support them tide over the difficulties.
pandemic.
Taking a bank loan is positively correlated Owners may consider taking a bank loan to solve the Implementing differentiated bank loans interest
with the recovery performance of SMEs pandemic-induced financial problems and support policies to promote businesses’ recover based on their
after the pandemic. them tide over the difficulties. different characteristics.
Businesses that received tax relief had better Owners may consider actively applying for tax relief to Implementing differentiated tax deduction rate policies
opportunities to avoid closure than solve the pandemic-induced financial problems and to promote businesses’ recovery based on their
those that were not given it. support them tide over the difficulties. different characteristics.
Owners with higher industrial experience Owners should pay attention to the accumulation of N/A
had better opportunities to achieve their industrial experience during normal operations in
good recovery performance after the addition to the context of the pandemic.
pandemic.
Businesses with large employees or geared Owners should pay attention to the expansion of the N/A
toward a large market could facilitate SMEs’ scale.
their recover after the pandemic.
Financial conditions before the pandemic Owners should promote performance and financial N/A
had positive impact on post-pandemic improvement during normal operations in addition to
recovery of SMEs. the context of the pandemic.
(1) The recovery performance of SMEs after the pandemic exhibits a positive spatial autocorrelation on the whole, however, there
are significant differences in spatial correlations among different local regions.
(2) Self-media marketing, borrowing money from family or friends, requesting bank loans and tax relief are effective measures to
avoid closure following a pandemic.
(3) The success of response measures to the pandemic varies with in the characteristics of both owner and business’s type.
The findings of the current study provide important implications (see Table 7). For SMEs’ owners, it is suggested that many
operational activities must be moved online during the pandemic period, and the influence of their products or services must be
expanded via self-media using the entertainment plus publicity model after the pandemic shock. Owners may consider borrowing
money from family or friends, taking a bank loan or actively applying for tax relief to solve the pandemic-induced financial problems
and support them tide over the difficulties. Owners need to consider the operate status of their neighboring SMEs when choosing the
exact reopening time or the specific place to relocate after a pandemic shock and an owner’s decision should vary based on the cluster
area in which it holds its business. For example, a business located in a cluster area with extremely poor post-pandemic recovery
performance should hold back its reopening time when the majority of its neighboring SMEs are poorly operated. Besides, in addition
to taking the more common pandemic response measures, owners should pay attention to the accumulation of their industrial
experience during normal operations, because growing it, it facilitates them to earn higher revenues and spread.
Since there may also be differences in the response measures to the pandemic by SMEs in cities of other countries, as well as
complementarity or substitutability of goods or services provided by neighboring SMEs in those cities, there is the possibility of po
tential spatial differences in the post-pandemic recovery performances of businesses. Thus, on top of all these suggestions, policy
makers should formulate guidelines and policies which should vary according to businesses located in different parts of the city, and
this principle may apply to most cities around the world that have experienced pandemic lockdowns. For example, in SMEs clusters
that have extremely poor post-pandemic recovery performance, policymakers should focus on building the regional business envi
ronment rather than just providing policy support to individual enterprises. In addition, the implementation of bank loans and tax
relief policies can help promote businesses’ recovery after multiple waves of pandemic shocks. However, the specific loan interest rate
and tax deduction rate need to be differentiated according to the characteristics of different firms and owners.
Although the current study provides a quantitative analysis of long-period pandemic shocks, there are still some shortcomings that
will have to be further investigated in the future. Firstly, the current study examines a sample of SMEs in one county of China, and thus
these results should be compared with those related to other countries or cities in China and worldwide. Therefore, future studies
should expand the study area and the sample size to get further insights associated to this context. Secondly, the current study focuses
only on SMEs in the manufacturing, wholesale/retail, and service sectors, and given the possible differences in the performance of
recovery associated with businesses of different sizes in different sectors after the pandemic, it is worthwhile for future research to
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investigate the performance of more segmented businesses in the aftermath of the pandemic and their recovery rate. Finally, although
the current study focuses on the impact of the five COVID-19 waves lasting two years, the singular rate or recovery after each single
wave is ignored. Therefore, exploring even each single scenario and those in series, may lead to dissimilar and additional outcomes.
CRediT authorship contribution statement
Fan Li: Writing – review & editing, Writing – original draft, Visualization, Validation, Software, Methodology, Investigation,
Formal analysis, Data curation, Conceptualization. Matteo Rubinato: Writing – review & editing, Writing – original draft, Supervi
sion, Methodology, Investigation, Formal analysis. Tao Zhou: Writing – review & editing, Writing – original draft, Visualization,
Validation, Supervision, Software, Resources, Project administration, Methodology, Investigation, Funding acquisition, Formal
analysis, Data curation, Conceptualization.
Declaration of competing interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to
influence the work reported in this paper.
Data availability
Data will be made available on request.
Acknowledgements
This research work is supported by the National Natural Science Foundation of China (No. 72304064), Guangdong Basic and
Applied Basic Research Foundation (No. 2022A1515110339) and Guangdong Provincial Key Laboratory of Intelligent Disaster Pre
vention and Emergency Technologies for Urban Lifeline Engineering (2022) (Grant No. 2022B1212010016).
Appendix 1
Questionnaire on business operations during the pandemic
(1) What is your age?
A. 20 or below; B. 21–35; C. 36–50; D. 51 and above
(2) What is your gender?
A. Male; B. Female
(3) What is your education?
A. Primary school or below; B. Junior middle school; C. High school/junior college; D. University degree or above
(4) How many years have you operated your business?
A. Less than half a year; B. 0.5–2years; C. 2–3.5years; D. 3.5–5years; E. More than 5 years
(5) How do you think the business climate will be in the future compared to the present?
A. Much better; B. A little better; 3. About the same; 4. A little worse; 5. Much worse
(6) What is your business type?
A. Manufacturing; B. Wholesale/retail; C. Service; D. Others
(7) Is your business owned or leased facility?
A. Owned facility; B. Leased facility
(8) What is the primary market your business served?
A. National; B. Provincial; C. Municipal; D. Town; E. Local
(9) How many full-time employees does your business have?
A. 1 full-time employees; B. 2–4 full-time employees; C. 4–10 full-time employees; D. 10 or more full-time employees
(10) What was the financial condition of your business before the pandemic (before the end of 2019)?
A. Extremely good; B. Relatively good; C. Moderate; D. Relatively bad; E. Extremely bad
(11) How is your business’s profitability at present compared to before the pandemic?
A. Better; B. About the same; C. Worse
(12) How many waves of pandemic lockdown (businesses in the lockdown area were required to close during the outbreak) have
your business experienced during the three-year outbreak period (end of 2019 - end of 2022; round 1 roughly June 2021–July
2021, round 2 roughly August 2021–April 2022, round 3 roughly June 2022, round 4 roughly July 2022, round 5 roughly
August 2022–December 2022)?
A. 5 waves and above; B. 4 waves; C. 3 waves; D. 2waves; E. 1wave; F. 0 wave
(13) Which of the following measures did you take in response to the pandemic? (Multiple choice question)
A. Layoffs; B. Technology reform and innovation; C. Online transformation (The online business model was not adopted before
the pandemic, but only after.); D. Opening branches; E. Purchase of anti-pandemic materials; F. Self-media marketing; G. No
measures taken
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(14) Which of the following assistances has your business received in response to the pandemic? (Multiple choice question)
A. Borrow money from family or friends; B. Bank loans; C. Tax relief; D. Rent relief; E. Advance goods from suppliers; F. No
assistances
Appendix 2
Statistics description for variables
Indexes Coding scheme Proportion (number)
Business recovery performance 0 = closed 19.4%(n = 115)
1 = performance is worse after the pandemic than before the pandemic 33.1%(n = 196)
2 = performance after the pandemic was like that before the pandemic 28.0%(n = 166)
3 = performance is better after the pandemic than before the pandemic 19.4%(n = 115)
Business response measures Whether the business has taken the following COVID-19 response measures?
Layoffs 1 = yes 9.1%(n = 54)
0 = no 90.9%(n = 538)
Technology reform and innovation 1 = yes 5.4%(n = 32)
0 = no 94.6%(n = 560)
Online transformation 1 = yes 13.7%(n = 81)
0 = no 86.3%(n = 511)
Opening branches 1 = yes 2.4%(n = 14)
0 = no 97.6%(n = 578)
Purchase of anti-pandemic materials 1 = yes 95.6%(n = 566)
0 = no 4.4%(n = 26)
Self-media marketing 1 = yes 8.3%(n = 49)
0 = no 91.7%(n = 543)
Borrow money from family or friends 1 = yes 14.4%(n = 85)
0 = no 85.6%(n = 507)
Bank loans 1 = yes 11.8%(n = 70)
0 = no 88.2%(n = 522)
Tax relief 1 = yes 14.0%(n = 83)
0 = no 86.0%(n = 509)
Rent relief 1 = yes 18.1%(n = 107)
0 = no 81.9%(n = 485)
Advance goods from suppliers 1 = yes 5.2%(n = 31)
0 = no 94.8%(n = 561)
Control variables Owner characteristics and business characteristics
Age 1 = 20 or below 0.5%(n = 3)
2 = 21–35 36.3%(n = 215)
3 = 36–50 53.2%(n = 315)
4 = 51 and above 10.0%(n = 59)
Gender 1 = male 48.3%(n = 286)
2 = female 51.7%(n = 306)
Education 1 = primary school or below 8.4%(n = 50)
2 = junior middle school 40.4%(n = 239)
3 = high school/junior college 40.9%(n = 242)
4 = university degree or above 10.3%(n = 61)
Industry experience 1 = less than half a year’ experience 0%(n = 0)
2 = 0.5–2years’ experience 7.1%(n = 42)
3 = 2–3.5years’ experience 10.3%(n = 61)
4 = 3.5–5years’ experience 29.2%(n = 173)
5 = more than 5 years’ experience 53.4%(n = 316)
Owner’ confidence in the business climate 1 = extremely confident 6.1%(n = 36)
2 = relatively confident 31.9%(n = 189)
3 = moderate 60.2%(n = 356)
4 = little confident 1.5%(n = 9)
5 = no confident 0.3%(n = 2)
Type of business 1 = manufacturing 1.0%(n = 6)
2 = wholesale/retail 54.1%(n = 320)
3 = service 39.5%(n = 234)
4 = others 5.4%(n = 32)
Owned or leased facility 1 = owned facility 10.5%(n = 62)
2 = leased facility 89.5%(n = 530)
Primary market 1 = national 6.6%(n = 39)
2 = provincial 3.7%(n = 22)
3 = municipal 14.2%(n = 84)
4 = town 44.6%(n = 264)
5 = local 30.9%(n = 183)
Business size 1 = 1 full-time employees 41.6%(n = 246)
2 = 2–4 full-time employees 24.3%(n = 144)
(continued on next page)
15
F. Li et al. International Journal of Disaster Risk Reduction 106 (2024) 104427
(continued )
Indexes Coding scheme Proportion (number)
3 = 4–10 full-time employees 20.4%(n = 121)
4 = 10–50 full-time employees 13.7%(n = 77)
Pre-pandemic financial condition 1 = extremely good 4.7%(n = 28)
2 = relatively good 43.6%(n = 258)
3 = moderate 45.1%(n = 267)
4 = relatively bad 5.7%(n = 34)
5 = extremely bad 0.9%(n = 5)
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