Economic Anxiety and the Performance of SMEs during COVID-19: A Cross-National Study in Kuwait

Abstract

The focal aim of the project was to assess the economic anxiety (EA) and the performance of small and medium enterprises (SMEs) during partial and full lockdowns in Kuwait. The challenges facing the SMEs during COVID-19 and the potential solutions were also explored. The call for this vital investigation was due to the global economic fallout and the shocking drop within the marketplace caused by the COVID-19 pandemic. A descriptive approach was used for online survey design to collect datasets from 147 SMEs spanning all governorates of Kuwait in the period between March and June 2021. It included sociodemographic data, economic anxiety perception, potential challenges and solutions to SMEs, and SMEs’ performance. The data analysis using SPSS 25 showed that 78.2% of the SMEs were affected directly by the COVID-19 pandemic, and about 83% were affected negatively by the COVID-19 pandemic. In comparison, only 12.2% experienced a positive impact, mainly medical, technology, social media, food supplies, and delivery or logistics industries. With great concerns of SMEs for all dimensions related to economic anxiety (with an average of around 3.95), the greatest concerns were the financial and cash flow, followed by labor shortage (an average between 4.51 and 5.00). The results also showed that most of the performance indicators for the SMEs were low (with an average of less than or equal to 2.5), and more than 66% of them worked fewer hours during the pandemic; the number of operating hours was dropped dramatically. More than 74% of the SMEs used technology in more than 20% of their activities, representing an increase in using technologies of about 44%, and about 25.2% used social networks in more than 80% of their activities. The performance of SMEs is also found to be significantly and positively correlated with the economic anxiety levels, with a correlation coefficient of 0.186. The findings revealed significant and crucial outcomes for policymaking, decision-makers, and governmental agencies to build recovery plans and proper actions needed to manage the consequences caused by the disaster against the economic and other developments within the context of SMEs. Overall, there is a clear need to find ways and customize operations to adapt to the new work modes that require social distancing, online operations, and site management. In addition, new alternative modes of SMEs work follow to compensate for the lower working hours from the office and increased online working from home.

1. Introduction

Since the World Health Organization (WHO) declared the outbreak of coronavirus disease (COVID-19) as a global pandemic in January 2020, all countries worldwide deemed a public health emergency and imposed lockdowns, social distancing, and temporary closure of businesses and services. Small and medium-sized enterprises (SMEs), representing a crucial market growth cluster, faced various operational challenges concerning cash flow, workforces, finance, supply chain, and sustainability. The global impact of the COVID-19 pandemic was far beyond public health as it hit the global economy and international trade. It necessitated the prompt implementation of new economic policies and strategies to sustain the economy and market continuity [1,2].

Kuwait is the world’s ninth-largest producer of crude oil, which distinguishes the country as a high-income economy. The country is considered one of the richest countries globally by gross national income per capita, with a total population of 4.27 Million [3]. According to MARKAZ, 90% of private companies in Kuwait are classified as SMEs, reaching around 25,000 private companies. Like other Kuwaiti businesses and organizations, they were affected by the pandemic, which derived the authorities to enforce partial and full lockdowns during the peak periods of the pandemic and may experience some levels of economic insecurity or instability, i.e., economic anxiety (EA). The performance of SMEs, both formal and informal, can particularly contribute to the growth and development of the economy and job creation [4]. This research aimed to answer the following critical questions: (i) Up to what extent do SMEs in Kuwait perceive the EA or insecurity of COVID-19? (ii) Up to what extent the performance of SMEs in Kuwait is affected by the COVID-19 pandemic during the partial and full lockdown, as well as after the lockdown? (iii) What are the main challenges facing the SMEs during COVID-19 and the potential solutions to meet those challenges?

Regarding the importance of the research outcomes, it can be witnessed that appraising the performance of SMEs and understanding their perception toward the economic anxiety caused by COVID-19 can facilitate managing the income deficiencies, influence the economic curing strategies, and inspire innovative dealing with the economic challenges driven by the COVID-19 pandemic [2,4]. In addition, identifying those measures and strategies to deal with and reduce the levels of economic anxiety among the SMEs in Kuwait is critical for economic development and growth; it is well known that those businesses make a significant share of the marketplace in Kuwait. Studying these issues is also important as it provides understanding by the academic community, policymakers, practitioners, and businesses. For example, policymakers and businesses would understand their current situation and the consequences of their actions and strategies with implications on reaching higher performance.

1.1. COVID-19 Pandemic and Economy Anxiety

According to the Worldometers which provides online statistics for coronavirus infection, the total number of worldwide infections of COVID-19 by the end of November 2021 was approximately 260 million, and the total number of deaths was about 5.2 million cases. As for Kuwait, the total number of infections was 413,000 cases and 2465 deaths. In response to the accelerated spread of COVID-19, governments worldwide, including Kuwait, have taken unprecedented control and preventive measures. Most businesses were closed, various events were canceled, travel restrictions have been applied, and a 14-day quarantine for individuals entering the country has been imposed. Such strict measures led to significant disruptions in transportation, services, and manufacturing industries, causing reductions in the economic growth and income of businesses with a substantial rise in the unemployment and financial distress levels [1,2,5,6].

The intensity of the pandemic had fluctuated between peaks and troughs but caused a significant worldwide contraction in the gross domestic product (GDP). It hit the global economy and dropped the economic growth rates and the financial markets, causing big economic uncertainty. It has been estimated that the consequences of the COVID-19 pandemic on the local and global economy are to be much worse than the effects caused by the Great Depression in the 1930s and the global financial crisis (GFC) in 2008 [6]. As a result, alarming economic challenges of the COVID-19 faced leaders, economists, policymakers, and the global workforce [7].

Largely, a high level of economic anxiety represents feelings of economic uncertainty or insecurity. In particular, there is a high level of a psychological state of concern experienced by individuals, business entities, and even governments concerning the economic conditions, future status, and behaviors during the pandemic; a case of suffering economic anxiety (EA) or insecurity [2,8].

The concept of EA is critical for SMEs as this may impact the behaviors, activities, and sustainability of such businesses. However, the economic security of an enterprise is defined as a complex status that characterizes the ability of the systems and setups at the enterprise to withstand the destabilizing effects of internal and external factors [9]. This is to ensure the efficient use of its resources (capital, personnel, technology, information, technology, etc.), explore existing market opportunities (competitiveness), and fulfill other statutory tasks in the future. In many cases, economic anxiety was interchangeably used with economic insecurity and sometimes with economic hardship or economic uncertainty to describe the psychological state of concern displayed by individuals, entities, and parties over conditions or crises in the economy. Thus, economic anxiety usually tends to occur when growing markets unexpectedly crash and stay in a recessionary state for periods longer than expected, regardless of the causing reasons [2].

1.2. The Performance of SMEs during the Pandemic

Small and medium enterprises are businesses whose personnel numbers and revenues fall below certain limits. These limits vary from one part of the world to another. Globally, there is no standard or specific definition of SMEs; yet, the common parameters used to classify a business as SME cover the business’s quantitative characteristics, including the number of employees, annual income or turnover, financial capital, assets, and investments. The abbreviation “SME” is commonly used internationally by many organizations such as the World Bank, the European Union, the United Nations, and the World Trade Organization, as in this research. SMEs are considered a prime contributor to the global economy and employment as they drive innovation and create diversified job opportunities [8]. For example, according to the National Federation of Self Employed and Small Businesses in the United Kingdom, there were 5.5 million small businesses (with 0 to 49 employees) at the start of 2021, where SMEs account for 99.9% of the British business population (5.6 million businesses) and three-fifths of the employment.

Over 95% of the worldwide businesses are SMEs, generating around 50% of global GDP. The interest in SMEs and their role towards job creation and global economic growth has increased after the global financial crisis of 2008–2009. In 2011, it was estimated that formal (officially registered) and informal SMEs could generate up to USD 10 trillion to the world economy representing around 90% of the overall global businesses. They can create more than 50% of jobs or employment opportunities [9]. The very recent literature provides evidence for the hard economic impact of the COVID-19 pandemic on SMEs; this potential impact is critical and should not be ignored by governments and policymakers [10,11].

These studies also show how SMEs have been hit harder by the crisis than larger firms. For example, a study conducted with a population of 995 Chinese SMEs by Tsinghua University and Peking University reported that 85% could not survive for more than three months during the COVID-19 epidemic [12]. Also, it is indicated that more than 70% of SMEs in China expect significant negative impacts on their revenues or performance [13].

On the other hand, a study was conducted at the US National Bureau of Economic Research (NBER) and surveyed more than 5800 US SMEs, indicate that nearly 43% of the SMEs were already temporarily closed. Most businesses reduced 40% of their employees, and 75% of the respondents indicated that they have only two months or less in cash in reserve [14]. In addition, according to a report by the Centre for Entrepreneurship, SMEs, Regions, and Cities (CFE) at the Organization for Economic Co-operation and Development (OECD) in July 2020, the evidence on the COVID-19 crisis impacts on SMEs from business surveys indicated severe disruptions and concerns among those enterprises [15]. Table 1 illustrates a shortlist of the outcome of 41 worldwide surveys on the impact of COVID-19 on SMEs, where more than half of those SMEs face severe losses in revenues, and about one-third fear being out of business within a month in case of no further support is provided. As it can also be seen in Table 1, the Organization for Economic Co-operation and Development showed that SMEs in most worldwide countries experience different forms of economic impacts on their businesses, leading to feeling or predicting insecure future expectations and economic anxiety.

Table 1. The 2020 surveys on the impact of COVID-19 on the performance of SMEs.

Country	Date (2020)	Impact on Business	Expectations
Belgium	18 March	Around 75% reported declines in their turnover	50% fear not to cope up with their costs in the short term
	3 April	Around 40% of businesses face more than 74% of a drop in their revenue	Around 10% of businesses are likely to experience bankruptcy
Canada	16 March	Around 50% of businesses drop in sales	More than 24% expect not to survive longer than a month
	24 March	More than 59% experience a significant impact	More than 33% expect to be out of business in a month
	4 May	More than 80% of small businesses face negative impact on their operations	More than 31% worry about the sustainability of their business over the 12 months
	7 April	Around 90% of small businesses were affected	More than 33% suffer reserves for more than few weeks
China	10 February	More than 79% of SMEs have not resumed operations yet	More than 33% of businesses are out in a month, another 33.3% are out in two months
Germany	24 April	About 58% of SMEs face on average around 50% a drop in their turnover	About 50% of SMEs have only two months liquidity reserve
Korea	17–20 March	More than 60% have been impacted	More than 41% fear being out of business in 3 months, and more than 69% in six months
Netherlands	20 March	Half of start-ups lost significant revenue	Half of start-ups expect to be out of business within three months
New Zealand	20 June	More than 70% of SMEs have taken a revenue hit by COVID-19	About 39% of SMEs experience feeling fear closing down
Poland	10 March	More than 33% of SMEs face increasing costs and reduced sales	Around 27% of SMEs already encounter cash flow problems
Portugal	6–10 April	About 37% of businesses face a drop of more than half in their production.	Around half of businesses do not have resources for more than two months
Several Asian countries	31 March–6 April	About 30% of SMEs are expected to lay off half of their employees	About half of SMEs have a month or less of cash reserves
Thailand	15 May	About 90% of businesses expect extreme revenue loss	More than half of small businesses expect to close down if containment rules continue
UK	12 March	More than two thirds of businesses experience serious cash flow problems	More than 33% fear being out of business in a month
	8 April	More than 36% of firms expect to lay off more than 75% of their staff next week	6% of firms are out of cash, and about 57% have only three months reserves or less
	13 May	More than 36% of firms are considering, or have already made, redundancies	More than 40% of firms have temporarily closed, and 35% fear they will not reopen again
USA	19 March	More than 95% of firms have been affected	More than half indicate not being able to survive three months
	7 April	About 90% of small businesses are affected	More than 33% lack the reserves to survive more than few weeks
	15–22 April	More than 60% of small businesses experience a drop in their revenues	More than 30% cannot stay open longer than three months

Source: Reproduced from [16].

Table 2 provides an overview of SMEs classification in Kuwait based on the number of employees, assets size owed by the enterprise, and the anticipated revenue (UNDP, 2011). Although SMEs provide and create jobs and employment opportunities for Kuwaitis and expatriates, their contribution to the country’s GDP is around 3% only, which is a low figure in comparison with the situation in other emerging economies in the Middle East (e.g., around 40% in UAE) [17]. It should be mentioned here that the State of Kuwait is part of the Gulf Cooperation Council (GCC) countries, including Saudi Arabia, Qatar, United Arab Emirates, Bahrain, and Oman, which are oil-producing countries.

Table 2. The SMEs classification in Kuwait.

Class of Business	Employment (Nationals)	Annual Turnover	Annual Balance Sheet (Total)
Micro	<10	≥KD 0.2 million	≥KD 0.2 million
Small	<50	≥KD 1 million	≥KD 1 million
Medium	<250	≥KD 5 million	≥KD 5 million

As reported by the Kuwait Financial Center and MARKAZ [4], 90% of the private companies in Kuwait are considered SMEs; around 40% of those businesses are in wholesale, retail, hotels, and restaurants, and about 33% in the construction and the industrial sectors. Most of the SMEs in Kuwait were affected by the COVID-19 pandemic due to the restrictions and lockdowns imposed by the government. They suffered limited cash flow and limited cash reserve. In addition, most businesses suffered a shortage of expatriate labor that was stuck in their home countries.

1.3. Theoretical Approaches and Responsive Strategies

Due to the diversity and changing nature of challenges faced by firms, there is no certain theory in the economic literature explaining the impact of those challenges with the strategic survival approaches to be adopted by firms to face such challenges [18]. Yet, several theories have been reported by scholars, including the Keynesian business cycle theory explaining the business cycle proposed during the economic crisis, the theory of comparative advantage as a tool to modify the production system to overall competition system, the generic strategies as proposed by Portes to identify and adjust the strategic position of the firm [19].

Overall, most of those and other theories highlight the importance of improving or achieving the stability conditions of firms through enhanced pathways for competitiveness [20,21,22,23]. In terms of SMEs and within the context of this work, reaching low-cost with higher levels of attributes of production, i.e., price and quality, remain of importance to gain a competitive position. Yet, such an approach requires a market and economy with marginal efficiency of capital and effective demand. Furthermore, the theories suggest that short-term and medium-term actions within the context of the COVID-19 pandemic such as digitalization and distance working, among others, may enable the SMEs to cope with the difficulties caused by this global crisis [24,25].

2. Research Focus

2.1. Research Problem

As mentioned by MARKAZ [4] that the “COVID-19 pandemic was a black swan event that brought the SME segment in Kuwait to a standstill”. While the number of infected cases with COVID-19 continues to increase, large-scale losses will be experienced by individuals, businesses, and governments; those losses are not limited to human lives but also economic growth, stability, and security.

However, the extent to which the SMEs in Kuwait perceive this event and the resulting economic insecurity and consequences with the impact levels on their performance remain of critical importance for decision making and policymaking; yet also remains unknown. Within the context of the COVID-19 pandemic in Kuwait, this study attempts to uncover the perception of SMEs towards economic anxiety or insecurity and their performance. This work aimed to answer the following critical questions: (i) Up to what extent do the SMEs in Kuwait perceive the EA or insecurity of COVID-19? (ii) Up to what extent is the performance of SMEs in Kuwait affected by the COVID-19 pandemic during the partial and full lockdown, as well as after the lockdown? (iii) What are the main challenges facing the SMEs during COVID-19 and the potential solutions to meet those challenges?

2.2. Objectives of the Study

Our main interest is to investigate the critical implications of the COVID-19 pandemic on the Kuwaiti SMEs in terms of the perception of economic anxiety (or economic insecurity) as experienced by the SMEs in Kuwait during the COVID-19 and the performance of those SMEs within the context of the pandemic during different forms of lockdowns. Therefore, this work in due course attempts to achieve the following focal objectives:

• Assess the levels of economic anxiety or insecurity triggered by COVID-19 as perceived by the SMEs in Kuwait.
• Appraise the performance of SMEs in Kuwait during the partial and full lockdown and after the lockdown imposed by the COVID-19 pandemic.
• Identify the potential measures and practices for policymaking to mitigate the levels of economic anxiety experienced by the SMEs in Kuwait and improve their performance and abilities.

2.3. Research Hypotheses

SMEs in Kuwait like in other parts of the world, are expected to experience negative consequences due to the COVID-19 pandemic and the health or prevention measures related to this pandemic. The attempt is to test the following hypotheses:

H1: 

The Kuwaiti SMEs perceive high levels of economic anxiety or insecurity due to the COVID-19 pandemic.

H2: 

The performance of SMEs in Kuwait is positively associated with the magnitude of the lockdown (i.e., first partial, full, and second partial lockdown) required due to the COVID-19 pandemic.

3. Materials and Methods

3.1. Measurement Instruments

The main aim of the measurement instrument is to obtain error-free data as much as possible to test the hypotheses and enable making the highest quality of prediction [26,27].

At its core, the measurement instrument involves rules for assigning numbers or values to objects to represent quantities of attributes or characteristics. Therefore, most of the variables used to develop and articulate the project framework are well investigated. The measurement scales are developed based on the relevant literature or earlier studies in the domain.

Based on the construct domain, 16 items for the economic anxiety (insecurity) and 37 items for the SMEs performance were generated from the relevant literature. For those constructs that directly fit the context of this study, the measurement items were adopted from established measurements reported in earlier studies. Then, they were adapted by translating them into the Arabic language; the measurement scale may have changed to a five-point Likert scale.

However, when the measurement items were unsuitable or did not fit with the context of this project, the items were adopted with slight modification where necessary; few changes can be made and/or constructed based on the expert suggestions and through team members’ discussions.

3.2. Economic Anxiety Operational Definition

In this study, economic anxiety for SMEs is conceptualized within the COVID-19 pandemic and operationalized based on earlier studies with adjustments to fit the SMEs in Kuwait. Principally, research constructs comprise a set of dimensions and can be measured using multiple items [28]; this can minimize errors associated with a single-item scale. However, and up to the best of our knowledge, there is no scale to measure economic anxiety in the domain of SMEs.

However, most of the available literature considered the economic anxiety for individuals and households rather than businesses. Indeed, some earlier work introduced some indexes in this domain to measure economic anxiety [2], economic uncertainty [14], and economic shocks [29]. In contrast, others considered all businesses, not particularly SMEs or even specific industries, rather than SMEs in any domain. Therefore, economic anxiety as a key construct in this study is compiled and reproduced from earlier literature to involve four key dimensions: general economic well-being, financial savings and expenses, employees and products, and market [2,27,28,29,30].

In all cases, the issue of anxiety as a psychological concept comes from feeling anxious about experiencing or predicting some concern or due to some event. Such matter in the case of this project may involve economic hardship, shock, instability, uncertainty, sentiment, expectations, insecurity, etc. The constructs, dimensions, and number of items to identify the economic anxiety are shown in Table 3.

Table 3. Research constructs and number of items.

Constructs	Dimensions	No. of Items	Relevant Sources
Economic anxiety (insecurity)	General wellbeing	16 items	[2,31,32,33,34,35,36,37,38,39,40]
	Savings and expenses
	Employees and products
	Market	4 items/dim.
SMEs’ performance	Financial measures	5 items	[41,42,43,44,45]
	Non-financial measures	20 items
	External factors	12 items

3.3. SMEs Performance Operational Definition

Performance measurement is defined as “a set of systems of metrics used to quantify both the efficiency and effectiveness of actions” [46]. The concept was also defined as “the evaluation of how well organizations are managed and the value they deliver for customers and stakeholders” [47]. Based on those definitions, there should be a set of key performance indicators (KPIs) to measure the performance of SMEs involving variables and entities of interest and comprising relevant issues of concern. There is no standard performance measure with the SMEs domain. Many studies suggest that it would be better to use both financial and non-financial measures [47,48,49,50,51,52,53]. While financial indicators such as sales and profit growth capture short-term changes calculated annually, non-financial performance such as knowledge acquisition and new products or services developments highlight long-term improvements [53].However, it is argued that non-financial performance measures provide a clearer overall view of performance needed by decision-makers [54].

The criteria to measure the performance of SMEs might vary from industry to industry and from one country to another due to complex variations in marketplaces, legal systems and impact of different crises [55,56]. Eventually, and for this study, three categories of measures will be considered: financial, non-financial (with some dimensions), and external (with some dimensions). The constructs, dimensions, and some items to measure the SME’s performance are shown in Table 3.

3.4. Sociodemographic Variables

The data collection instrument included several questions to collect data about several social and demographic factors of the individuals filling the survey and other sociodemographic factors related to the businesses.

The factors of the participants as individuals included gender, age, level of education, and the relationship to the business. Other questions related to the participants as businesses included size and industry, year of establishment, the current financial capital and its source, the duration and patterns of operating time, weekly hours of work before and during COVID-19, and other questions. In particular, the survey contains a set of questions that covers the impact of COVID-19 on SME’s work, the challenges, and the perceived solutions for such challenges.

3.5. Sampling and Participants

This study focuses on the SMEs in Kuwait, representing about 90% of private companies in Kuwait. They are expected to be more vulnerable to risks during the COVID-19 pandemic than larger firms. Therefore, the study has a cross-sectional design where participants complete a self-report questionnaire on the economic anxiety index described in other studies [2,5,10]. It covers Kuwaiti SMEs regardless of the type of industry, products, services, or other variables. The survey is uploaded online using the Google Surveys service, and a link is distributed to the target audiences via WhatsApp, emails, and social media networks.

3.6. Data Collection and Procedures

The described earlier survey was utilized to involve more business subjects and enable more generalizations. With this quantitative approach, higher sample size was aimed to be reached. Causality then can be explored in a broader study of variables. The questionnaire was distributed electronically to business owners and partners, where three reach levels were adopted to obtain a satisfactory response rate.

Firstly, government organizations and large institutions are concerned with SMEs; secondly, licensed business incubators and accelerators; thirdly, direct contact with business owners through networking, word of mouth, and social media.

On the first level, the “National Fund for the Development of Small and Medium Enterprises” in Kuwait was approached to access the SMEs financed by the Fund. It is a government-independent public corporation with a total capital of KD 2 billion that finances up to 80 percent of the capital for feasible small and medium projects.

We managed to access their database and contact the business owners who received finance from the National Fund. In addition, the Arab Planning Institute (API), which is based in Kuwait, was approached to distribute the questionnaire through its database for SMEs supported and trained by the API. It aims to enhance the role of SMEs in economic and social development in Kuwait and other Arab countries and is expected to provide proper support for data collection.

On the second level, renowned incubators and accelerators such as “Rise” and “Niu Collaborative Community” in Kuwait were approached. Meetings with the management and available entrepreneurs were conducted, and the purpose of the survey was explained. This paved the way to distribute the questionnaire to all entrepreneurs using the incubation services in various industries such as manufacturing, IT and communication services, food and beverage, training and education, engineering, health and medical, and other general services.

On the third level, and through social media like Facebook and other business networks, many entrepreneurs were approached and welcomed to be part of this study. They received links to complete the questionnaire online and shared it with their partners and industry links. About 186 individual SMEs received a copy of the questionnaire, and only 41 were not considered for further data analysis due to some issues due to missing data or answering with the same level, resulting in 147 fully completing the questionnaire to form the basis for the data analysis.

3.7. Statistical Analysis

Descriptive statistics, correlation, and analysis of variance (ANOVA) were used to analyze economic anxiety and SMEs’ performance. Descriptive statistics involved frequencies and percentages for categorical variables, and one-way analysis of variance (ANOVA) was used to assess differences among the SMEs groups subject to the sociodemographic data of the businesses. Furthermore, the goodness of fit and model validity and adequacy were checked using residuals and recursive residuals [57]. Correlation analysis was performed to understand the relationships between constructs and the dimensions. Statistical applications, statistical tests for reliability and validity of the data collection tool, factor analysis, and other statistics using SPSS (Ver 25) were used in this project.

4. Results

4.1. Sociodemographic Data of the Participants

Table 4 illustrates the sociodemographic data of the individual participants. It can be noticed that most participants are males (73.5%), most participants are young, 59.2% less than 45 years old, and most participants are married (63.9%). In addition, most participants gained an educational level with at least a bachelor’s degree (56.5%) or master’s degree (17%) or Ph.D. (4.8%), while the vast majority of participants are from Al Asimah and Hawalli (58.5%). The vast majority (94.6%) of participants are owners or partners, giving us more realistic data for the study. Those demographics show that the study involves individuals with varying attributes, revealing that the collected data are mostly from diverse experiences and perspectives, enriching the findings.

Table 4. Sociodemographic data of the sample in the main study (n = 147).

Characteristic	Group	Number	%
Gender	Male	108	73.5
	Female	39	26.5
Age	Less than 25	7	4.8
	25–34	32	21.8
	35–45	48	32.7
	45–54	31	21.1
	55–64	17	11.6
	More than 65	12	8.2
Marital status	Single	32	21.8
	Widowed	4	2.7
	Married	94	63.9
	Divorced	17	11.6
Education level	Diploma or less	32	21.8
	Bachelor	83	56.5
	Masters	25	17.0
	Ph.D.	7	4.8
Governorate	Al Asimah	47	32.0
	Hawalli	39	26.5
	Al Ahmadi	13	8.8
	Jahra	9	6.1
	Farwaniya	25	17.0
	Mubarak Al-Kabeer	14	9.5
Relationship to SMEs	Single owner	96	65.3
	Partner owner	43	29.3
	Worker or employee	8	5.4

4.2. Sociodemographic Data of the Businesses

Table 5 illustrates the sociodemographic data of the businesses involved in the study. The table shows that the vast majority (83.7%) of surveyed SMEs were affected negatively by the COVID-19 pandemic, 12.2% of SMEs were positively impacted by the pandemic, and 2.7% of SMEs indicated that they were not affected by the pandemic.

Table 5. Business sociodemographic data of the sample in the main study (n = 147).

Characteristic	Group	No	%
Pandemic effect	Positively	18	12.2
	Negatively	123	83.7
	No effect	4	2.7
	It cannot be determined	2	1.4
How the business is affected by the pandemic	Directly	115	78.2
	Indirectly	24	16.3
	No effect	4	2.7
	It cannot be determined	4	2.7
Size of business	Micro (national employees <10)	45	30.6
	Small (national employees <50)	78	53.1
	Medium (national employees <250)	24	16.3
Area of business or industry	Medical (practitioner, pharma, nursing, etc.)	10	6.8
	Engineering (industrial, oil, construction, etc.)	20	13.6
	Business (trading, accounting, marketing, etc.)	29	19.7
	Public services, military, police, security, etc.	9	6.1
	Communications, electronics, and technology	8	5.4
	Arts, culture, or entertainment	10	6.8
	Education, science, or agriculture	28	19.0
	Others	10	6.8
Business age	Less than 5 years	89	60.5
	Between 5 and 10 years	33	22.4
	Between 11 and 20 years	17	11.6
	More than 20 years	8	5.4
Capital amount	Less than 200 K (KD)	97	66.0
	Between 200 and 500 K	35	23.8
	500 K–1 m	10	6.8
	1 m–1.5 m	3	2.0
	1.5–2 m	2	1.4
	More than 2 m	2	1.4
Survival time	Less than 6 months	44	29.9
	6–12 months	53	36.1
	12–18 months	19	12.9
	18–24 months	7	4.8
	More than 24 months	24	16.3
Source of the capital	Personal or partnership investment	20	20.4
	Government grant	17	17.3
	Private companies or loans	6	6.1
	Bank loans	51	52.0
	Preferer not to answer	20	20.4
Financial support during the pandemic	Private	34	23.1
	Government support	22	15.0
	Private or governmental loans	18	12.2
	No support	69	46.9
	Preferer not to answer	4	2.7

On the other hand, in terms of direct and indirect influence, the analyses show that 78.2% of SMEs are directly affected by the pandemic, 16.3% are indirectly affected by the pandemic, and 2.7% cannot determine how the pandemic affected them. In addition, the vast majority (53.1%) of the SMEs are considered small enterprises, 30.6% are small to medium, and only 16.3% are medium enterprises.

Most SMEs (82.9%) started their business in less than ten years, with only 17% being in business for more than ten years but less than 20 years, and 89.8% of the surveyed SMEs have a capital than KD 500 K. The findings also illustrate that Bank loans fund 52% of SMEs, the government funds 17.7%, and the remaining ones were funded by other sources such as private, personal, and partnership investment.

Table 6 illustrates the operating periods and the number of hours before and during the business operation before and during the pandemic. Table 6 also illustrates an increase in the technology use and social media levels during the pandemic for business operations. It shows that before the pandemic, most SMEs (78.2%) were operating during the daytime and evenings. This reflects the common cultural orientation to work for two shifts in Kuwait due to its hot weather. However, this changed during the pandemic to working mainly during the daytime.

Table 6. Running the SMEs and working hours.

Nature of Business	Group	No	%
Operating hours before the pandemic	During the day only	26	17.7
	Evenings only	3	2.0
	Night only	0	0.0
	Day and evenings	115	78.2
	Evening and night	3	2.0
Operating hours during the pandemic	During the day only	105	71.4
	Evenings only	4	2.7
	Night only	1	0.7
	Day and evenings	11	7.5
	Evening and night	26	17.7
Number of operating hours—before	<40 h	15	10.2
	41–50 h	30	20.4
	51–60 h	52	35.4
	>60 h	50	34.0
Number of operating hours—during	<40 h	81	55.1
	41–50 h	45	30.6
	51–60 h	18	12.2
	>60 h	3	2.0
Technology increase in SMEs operations due to the pandemic	<20%	37	25.2
	21–40%	31	21.1
	41–60%	37	25.2
	61–80%	30	20.4
	>80%	12	8.2
Social network usage in SMEs operations due to the pandemic	<20%	28	19.0
	21–40%	25	17.0
	41–60%	29	19.7
	61–80%	28	19.0
	>80%	37	25.2

71.4% of the SMEs reported they were obliged to work for limited hours during the daytime, and only 17.7% of the SMEs reported working in the evening and at night. Most SMEs (64.3%) work more than 50 h a week, with an average of 53 h before the pandemic. However, during the pandemic, the number of operating hours dropped dramatically, resulting in 66% of SMEs working less than 40 h.

The overall average for the number of operating hours dropped down significantly to only 36 h. This is an almost 34% reduction in the number of operating hours due to the pandemic. In addition, most SMEs (74.8%) used technology more than 20% of their activities. The average increase of using technology in operating SMEs is 44%. The social media usage in SMEs operation increased more than using the technology in the SMEs operations with an average of 54%. The highest percentage of SMEs (25.2%) used the social networks more than 80%, while the other usage percentages are almost uniform with almost 20%.

4.3. Challenges Facing the SMEs during COVID-19 and Potential Solutions

Table 7 summarizes the challenges facing the SMEs as reported by the participants showed that the average of each item, standard deviation, and rank among the 17 proposed challenges are summarized. SMEs’ greatest concern (highest average, 4.51–5.00) is the financial and cash flow, followed by a labor shortage. The third concern in the list is the political, social, and religious challenges. Finally, the lowest challenge (0.00–1.50) was meeting the national and international demand and the technical or electrical problems.

Table 7. Descriptive statistics for SMEs challenges during the pandemic.

Item			Average	Stdev	Rank
	Labor shortages		3.51	1.63	2
	2. Logistics problems		2.95	1.56	8
	3. Marketing problems		3.09	1.39	5
	4. Financial or cash flow		3.88	1.33	1
	5. Distribution challenges		3.1	1.62	4
	6. Raw materials problem		2.85	1.63	12
	7. Production challenges		2.92	1.69	10
	8. Local or international competition		2.84	1.54	13
	9. Lack of market demand problem		2.94	1.62	9
	10. Import and export regulations		3.09	1.64	5
	11. Fulfilling local or international orders		2.53	1.61	16
	12. Fulfilling financial dues like salaries or rent		2.99	1.65	7
	13. Political, social, or religious challenges		3.15	1.64	3
	14. The increased cost of operations and productions		2.81	1.77	14
	15. Starting up of new products, services of markets		2.88	1.78	11
	16. Technical, technology, and electricity challenges		2.71	1.59	15
	17. Others		1.58	1.78	17
Aggregated average			2.93
Cronbach alpha		0.88
KMO		0.79	p-value < 0.001
Variance explained		65.3%

Table 7 also contains the sample size adequacy and reliability of factorial models was assessed using Kaiser–Meyer–Olkin (KMO) and Cronbach’s alpha. This factorial model is considered adequate since the sample adequacy measure (KMO test) was 0.79 (>0.5), and the percentage of variance explained was 65.3% (>50%).

Moreover, the operationalized challenges were reliable according to Cronbach α, which was greater than 0.6. The internal consistency of all groups of items related to challenges was tested using the Cronbach α coefficient. Internal consistency refers to the degree to which all items on a particular test measure the same attribute. Cronbach α coefficient is 0.88, which is higher than 0.7; it can be considered that this dimension’s internal consistency and stability has been achieved [57,58,59].

Based on the findings of the solutions as in Table 8, it can be seen that assessing the needs for supporting strategies, as well as with the obvious need for evaluating the challenges due to the impact of the COVID-19 pandemic on SMEs, is the most important and could be as first safeguarding tactic. This result supports our argument of the necessity of this study in this research project. The second strategy in the list was given to support SMEs (governmental or private) in paying the rent, freezing the loan payments, and then governmental or private support in paying wages.

Table 8. Descriptive statistics and strategies that should be used to help SMEs during the pandemic.

Item			Average	Stdev	Rank
	Easing of access and conditions of loans		3.87	1.38	6
	2. Postpone and moratorium on debt repayments		3.92	1.53	3
	3. Governmental support for direct debit payments		3.85	1.63	7
	4. Guaranteeing banking and non-banking loans		3.8	1.53	9
	5. Introducing schemes to monitor the impact of the crisis		4.03	1.25	1
	6. Promoting buying from local businesses by the government		3.68	1.66	10
	7. Providing governmental grants and subsidies for businesses		3.83	1.57	8
	8. Governmental support for employment wage and sick leave		3.88	1.42	5
	9. Public or private support for rent and business utility payments		4.01	1.34	2
	10. Increasing the available funding of the existing loan schemes		3.88	1.52	4
	11. Deferral or waiving of tax, social security, penalties, and governmental fees		3.54	1.63	11
	12. Introducing market restructuring policies enabling the adoption of new working methods and environments like technologies or teleworking		3.38	1.74	12
	Others		2.84	1.91	13
Aggregated average			3.73
Cronbach alpha		0.917
KMO		0.88	p-value < 0.001
Variance explained		65.33%

For the potential solutions, this measurement’s internal consistency and stability have been achieved where the calculated Cronbach α (0.917) coefficient is higher than 0.7 as standard baseline [59,60]. However, the internal consistency test revealed that the last two items were not significantly related to the suggested strategies to help SMEs during the pandemic.

This study also classified those challenges into three main categories, including the market financials and employment (CFE), environmental and legal (CEL), and production and process (CPP); this is also as considered by official market reports and according to marketplaces. The findings showed that all categories of challenges are present as reported by or according to our respondents. In particular, the financial problem is the most pressing than any other challenge, with the least challenging being production and processes.

Table 9 illustrates the descriptive statistics for the three categories of SMEs’ challenges during the pandemic. In the same manner, the solutions as reported by the participants were classified into three dimensions. According to the findings, the suggested solutions by the SMEs who participated in the survey for this study were uniformly distributed, i.e., the strategies under the classes of governmental, financial with the environmental and legal are with an average of approximately 4.1. However, the research team decided not to consider the findings for further details regarding this item. The assessment for the three categories is mostly the same, which may indicate filling the survey improperly.

Table 9. Descriptive statistics for classified challenges facing the SMEs during the pandemic.

	CPP	CFE	CEL	Overall
Mean	3.45	4.12	3.81	3.74
N	143	141	140	147
Std. deviation	0.867	0.724	0.822	0.719

4.4. Descriptive Statistics for Economic Anxiety

All factors in the economic anxiety scale (EAW, EAS, EAE, and EAM) are internally consistent and reliable, as described in Table 10. The overall Cronbach alpha for each factor is more than the recommended threshold (0.7). Respondents raised a high concern for all dimensions related to economic anxiety (all averages are above 3.5) with an overall average of 3.95 (Stdev = 1.01).

Table 10. Descriptive statistics for economic anxiety dimensions.

Item			Average	Stdev	Cronbach Alpha
EAW1	Feeling anxious about experiencing a major loss in financial income		4.03	1.17
EAW2	Feeling anxious about experiencing a low ratio of cost–return		4.09	1.19
EAW3	Feeling anxious about experiencing low levels of cash flow		4.04	1.22
EAW4	Feeling anxious about experiencing an uneasy future financial situation		4.16	1.14
Subgroup			4.08	1.10	0.94
EAS1	Feeling anxious about experiencing large out-of-pocket expenses		4.26	1.09
EAS2	Feeling anxious about experiencing unexpected or additional expenses		4.23	1.15
EAS3	Feeling anxious about experiencing limited future financial affordability		4.20	1.08
EAS4	Feeling anxious about experiencing inadequate liquid financial wealth		4.08	1.16
Subgroup			4.19	1.05
EAE1	Feeling anxious about experiencing low labor productivity or attendance		3.96	1.19
EAE2	Feeling anxious about experiencing instability in product prices		3.84	1.23
EAE3	Feeling anxious about experiencing limited levels of operational capacity		3.87	1.22
EAE4	Feeling anxious about experiencing limited ability toward getting or producing new products		3.50	1.38
Subgroup			3.79	1.08	0.95
EAM1	Feeling anxious about experiencing loss in market stability and volatility		4.02	1.18
EAM2	Feeling anxious about experiencing new market competitors and share		3.55	1.35
EAM3	Feeling anxious about experiencing new market threats and challenges		3.78	1.32
EAM4	Feeling anxious about experiencing fluctuation in supply–demand levels		3.62	1.33
Subgroup			3.74	1.17	0.884
Aggregated average			3.95	1.01	0.963
KMO		0.921		p-value < 0.001
Variance explained		79.0%

Their highest concern is for EAS with an average of 4.19 (Stdev = 1.05) and the least concern for EAM and EAE with an average of 3.74 and 3.79, respectively. Respondents were more concerned about unexpected additional expenses from their pockets. Their least concern was for experiencing new products and markets during the pandemic.

4.5. Descriptive Statistics of SMEs Performance

All factors for the SMEs performance scale and the overall construct (performance) as in Table 11 are internally consistent and reliable as the overall Cronbach alpha. Each factor is more than the recommended threshold (0.7). Most SMEs’ performance indicators are low (with an average of less than or equal to 2.5). These indicators (in descending order based on their averages) are EXP, FIN, NFP, EXM, and FIN. The moderate indicators are EXC, NFE, EXR, and NFC, averaging from 2.51 to 3.5.

Table 11. Descriptive statistics for SME performance.

Item			Average	Stdev	Rank	Cronbach Alpha
FIN1	The ratio of sales growth		2.19	1.14
FIN2	The ratio of cash flows		2.29	1.13
FIN3	Operating income or return on sales		2.21	1.05
FIN4	Net profit margin or product profitability		2.09	1.03
FIN5	The return on investment or capital		2.15	1.10
Subgroup			2.18	0.96	9	0.93
NFC1	The response time to customers		2.33	0.99
NFC2	The acquisition of new customers		2.17	1.00
NFC3	The level of customers’ satisfaction and loyalty		2.92	1.13
NFC4	The percentage of repeat customers		2.72	1.06
NFC5	The customers’ complaints		2.41	1.08
Subgroup			2.51	0.81	4	0.83
NFE1	Employees’ turnover and absenteeism rate		2.75	1.17
NFE2	Employee competency rate		2.96	1.00
NFE3	Average hours of employees’ training		2.55	1.12
NFE4	The result of the job satisfaction survey		2.85	1.08
NFE5	The levels of innovations by employees		2.65	1.01
Subgroup			2.75	0.83	2	0.83
NFM1	Growth in market share		2.26	1.17
NFM2	Stability of market supply and demand channels		2.54	1.16
NFM3	Promotions to new products		2.50	1.17
NFM4	Hours of operating activities		2.37	1.14
NFM5	Profitability ratio of sales		2.63	1.10
Subgroup			2.43	0.99	6	0.86
NFP1	Percentage of returned products		2.41	1.12
NFP2	The average age of production processes		2.24	1.04
NFP3	Average time of overdue processes		2.37	1.08
NFP4	Average of available inventory for processes		2.48	1.11
NFP5	Average of activities against the planned schedule		2.27	1.09
Subgroup			2.35	0.93	7	0.91
NFM1	Growth in market share		2.26	1.17
NFM2	Stability of market supply and demand channels		2.54	1.16
NFM3	Promotions to new products		2.50	1.17
NFM4	Hours of operating activities		2.37	1.14
NFM5	Profitability ratio of sales		2.63	1.10
Subgroup			2.43	0.99	6	0.86
EXM1	Energy consumption rate		2.52	1.03
EXM2	The ratio of recycling materials		2.01	1.04
EXM3	Amount of pollutant discharge		2.08	1.10
Subgroup			2.20	0.87	8	0.77
EXP1	Number of strategic partners		2.24	1.04
EXP2	Average of delivery and response by partners		2.45	1.09
EXP3	Average and quality of communications with partners		2.62	1.13
Subgroup			2.43	0.97	5	0.861
EXC1	Average of price performance		2.62	1.10
EXC2	Average of delivery performance		2.77	1.09
EXC3	Average of quality performance		2.96	1.03
Subgroup			2.79	0.95	1	0.86
EXR1	Levels of export/import obstacles		2.76	1.35
EXR2	Applying employment and payment laws		2.59	1.11
EXR3	Applying tax and market-related laws		2.40	1.11
Subgroup			2.58	0.96	3	0.724
Aggregated average			2.47	0.76		0.97
KMO		0.882			p-value <0.001
Variance explained		72.7%

4.6. Impact of Lockdowns on the Dimensions of the Study

Table 12 summarizes the negative impact percentage of the lockdown on economic anxiety and performance of SMEs. The highest negative impact is on the economic anxiety over the three lockdowns (first partial, full and second partial) with 65.9%, 74.1%, and 58.1%, respectively. As indicated, the negative impact in the second partial lockdown was less than the first one with 7.8% and by 16% from the full lockdown. There is no difference in the negative impact on SMEs’ performances between the first partial lockdown and the full lockdown with approximately 47.9%. This explains the high percentage of economic anxiety of SME owners during the lockdown as SMEs’ performance reduced by almost half.

Table 12. Impact of lockdown on the study dimensions.

	Dimension	Average %	Stdev
First partial lockdown	Economic anxiety	65.9	0.055
	Performance	47.9	0.058
Full lockdown	Economic anxiety	74.1	0.051
	Performance	47.1	0.058
Second partial lockdown	Economic anxiety	58.1	0.057
	Performance	40.7	0.057

The lockdowns’ negative impact on Economic Anxiety was above 50%, which is considered very high during the lockdowns (first partial, full lockdown, and second partial). The negative impact started with 65.9% (average) in the first lockdown, increased to 74.1% in the full lockdown, and decreased to 58.1% in the second partial lockdown, as indicated in Table 12. The lockdown has a less negative impact on performance than economic anxiety, with almost the same negative impact of 47% (average) in the first and the full lockdowns. While in the second lockdown, this impact decreased to 40.7%, as SMEs probably found new ways of improving their performance, as shown in Table 12.

4.7. Correlation Analysis among the Study Constructs

Table 13 summarizes the pairwise correlation analysis between “economic anxiety” and “SME performance”. Overall, SME performance is significantly and positively correlated with economic anxiety with a correlation coefficient of 0.186. SMEs’ performance is correlated with all economic anxiety factors except EAM. The highest correlated factor of EA is with EAE, with a correlation of 0.234. FIN and EXP are significantly and positively correlated with all economic anxiety factors except EAE. NFC, NFM, and EXM are significantly and positively correlated only with EAE. NFM and EXC are significantly and positively correlated with all EA factors.

Table 13. Pearson’s correlation matrix between SMEs performance and economic anxiety.

	1	2	3	4	5	6	7	8	9	10	11	12	13	14
1. SME PERFORMANCE	1
2. FIN	0.795 **	1
3. NFC	0.890 **	0.734 **	1
4. NFE	0.777 **	0.546 **	0.704 **	1
5. NFM	0.863 **	0.762 **	0.782 **	0.678 **	1
6. NFP	0.826 **	0.640 **	0.686 **	0.602 **	0.691 **	1
7. EXM	0.767 **	0.528 **	0.622 **	0.466 **	0.614 **	0.680 **	1
8. EXP	0.831 **	0.597 **	0.737 **	0.583 **	0.647 **	0.599 **	0.604 **	1
9. EXC	0.769 **	0.473 **	0.655 **	0.621 **	0.575 **	0.470 **	0.496 **	0.689 **	1
10. EXR	0.757 **	0.498 **	0.589 **	0.497 **	0.538 **	0.637 **	0.570 **	0.567 **	0.582 **	1
11. ECONOMIC ANXIETY ALL	0.186 *	−0.01	0.147	0.276 **	0.121	0.224 **	0.192 *	0.1	0.116	0.208 *	1
12. EAW	0.176 *	−0.051	0.137	0.303 **	0.095	0.166 *	0.158	0.096	0.195 *	0.197 *	0.914 **	1
13. EAS	0.173 *	−0.005	0.134	0.284 **	0.124	0.192 *	0.097	0.114	0.137	0.193 *	0.913 **	0.893 **	1
14. EAE	0.234 **	0.06	0.206 *	0.284 **	0.219 **	0.269 **	0.265 **	0.131	0.114	0.177 *	0.868 **	0.682 **	0.681 **	1
15. EAM	0.092	−0.042	0.055	0.135	0.004	0.173 *	0.16	0.024	−0.011	0.183 *	0.900 **	0.734 **	0.735 **	0.740 **

** correlation is significant at the 0.01 level (2-tailed). * Correlation is significant at the 0.05 level (2-tailed).

5. Discussion

The focal aim of this study was to assess the influence of the COVID-19 pandemic on the levels of economic anxiety experienced by the Kuwaiti SMEs and assess their performance during the partial, full, and second partial lockdowns practiced by the government. Although the vast majority of surveyed SMEs indicated that they were affected negatively by the COVID-19 pandemic, a small percentage (12.2%) of SMEs did very well during the pandemic, mainly those who are in the medical, technological, personal computers, social media, food supplies, and delivery or logistics industries. Entrepreneurs as owners or partners in SMEs who participated in the survey expressed a high level of economic anxiety (overall average of 3.95) concerning many factors such as low levels of cash flow, difficult future financial affordability or unexpected additional expenses, or future losses due to lack of resources, increased wages, decreased purchasing power, and changes in the supply and demand curve.

Economic anxiety is nothing but a state of insecurity that incites individuals and business owners to stay away from it by creating a safe environment to protect themselves from threatening future events [61,62,63,64,65,66].

The consequences of economic anxiety are more important for business analysts as they could lead to dramatic changes in the business. Entrepreneurs who experience a high level of economic anxiety could sell or close their businesses, or they could take action and make modifications in their business model to raise their economic security. Therefore, economic anxiety is associated with new decision making and movements which could disturb, protect, or enhance the business model.

On the psychological and personal levels, when individuals experience a high level of economic anxiety, it impairs cognitive functioning, triggers feelings of lack of control, and makes them more prone to various illnesses such as heart diseases and psychiatric disorders. This will increase pain as the foundation of human motivation is built mainly on having stability in life [62,63].

Data analysis indicates that the greatest concern for SMEs that elevated their economic anxiety was the shortage of cash in hand and shortage of future income cash and liquid assets. This likely results from lockdowns and ceases of interrelated and connected businesses that exchange cash in the value chain. On the other hand, the results indicated less EA and concern for experiencing new products and markets even though some products need to be customized to comply with health restrictions and social distancing. New product development could take a long time before being seen in the market. In addition, selling products in new markets could require shipping and transportation, which witnessed a sharp increase in prices.

There is a need to evaluate the challenges faced by SMEs during the pandemic and assess their impact on the business. Based on the data analysis, the highest challenge perceived by SMEs owners and partners is related to financials and employment. The lack of income cash and the continuing overheads such as rents and salaries to be afforded were major concerns for SMEs. They did not have enough financial reserves to cover pitfalls. In addition, all businesses in Kuwait suffered from the stranded labor overseas and those who stayed in their home countries, which affected the normal business operations and the value chain, particularly the distribution of products and services in Kuwait.

The results show that production and process were less challenging for SMEs than environmental and legal issues. This could be related to different levels of automated processes readily available for these SMEs and the locally available labor that did not travel out of Kuwait during the pandemic. Most entrepreneurs highlighted the importance of introducing schemes to monitor the impact of the crisis; this includes public or private support for rent and postponement of loan or debt repayments and governmental support for employment wage and sick leaves. These issues became prime challenges for any business in Kuwait that continued to pay rents during the lockdown. The government needs to reach a realistic compromise between healthy economies while controlling the pandemic and not destroying the societal health system.

The findings also illustrate that bank loans fund 52% of SMEs, the government funds 17.7%, and other sources funded the remaining ones. However, about half of the respondents (50.3%) indicated that they received some support from the government and private sources during the peak period of the pandemic. This is an interesting fact as it reflects the government initiative and the steps taken by SMEs to get some financial funds to support SMEs and consequently will relieve part of their economic anxiety.

In fact, SMEs work hard to sustain their operations and do extra hours than other organizations who work five days a week, equivalent to 40 h per week. In total, 71.4% of the SMEs reported that they were obliged to work for limited hours during the daytime, and only 17.7% of the SMEs reported working in the evening and at night. More than half of the respondents (64.3%) indicated that they used to work more than forty hours a week (average of 53 h/week) before the pandemic. However, this number dropped to an average of 36 h during the pandemic, which is relatively higher than the working hours of other organizations in Kuwait. SMEs are under pressure to cover their operational costs and make some profit. They also face tough competition from market incumbents who are generally more resilient to market pitfalls than SMEs.

The data analysis indicates an expected increase in the use of technology and online communication due to social distancing and varied levels of lockdowns. The average increase of using technology in operating SMEs was 44%. However, the use of social media channels for communication, marketing, and e-business has noticeably increased to an average of 54%. Many SMEs in Kuwait relied on Instagram as a platform for their e-store, and it became a prime distribution channel for their products and services. In fact, one-quarter of the SMEs indicated that they used social media networks for 80% of their day-to-day business operations. Kuwait enforced three major lockdowns, one of them as a full lockdown with only two hours a day left for people to walk close to their houses without using their cars. The highest percentage of the negative impact on the economic anxiety over the three lockdowns was 74.1% related to the full lockdown. This is a likely result, but interestingly, the lockdown has a less negative impact on the performance of SMEs than their perception of economic anxiety. This could be referred to as alternative modes of SMEs work followed to compensate for the lower working hours from the office and increased online working from home.

The Kuwaiti media even broadcasted that the business sectors particularly obtained financing of around USD 2.7 billion from the local banks just during the period between April and mid-June 2020. Therefore, a National Fund for the SMEs Development program (also known as SME Fund) has been proposed and funded about 870 SMEs in Kuwait. A new law was also proposed by the Council of Ministers (decision No. 455 on 3 March 2020) to support the SMEs and guarantee 80% of the loans by the governmental SME Fund program and by the Kuwaiti banks during the COVID-19 pandemic. Indeed, this decision could drive the cash flow and assist in payments for salaries, rents, and supply chains. Yet, the SMEs remain to obtain further support with more financial capital to secure their future, grow and recover their supply chain.

Interestingly, the performance of SMEs was measured by ten dimensions, which gives a clearer picture of the performance during the pandemic. The return on business investment was quoted low by the participants, which could be referred to the higher operating costs and shortage of revenues. Customers showed understanding of the long waiting times and the shortage of human resources. Hence, their satisfaction level did not drop to multiple levels, and nor did their complaints increase to worrying levels. However, interestingly, the employees’ competence and innovative approaches or initiatives have increased during the pandemic. This could be referred to as the need to find ways and customize operations to adapt to the new work modes that require social distancing, online operations, and site management.

6. Conclusions

The emergence of the COVID-19 pandemic caused a massive economic shock in developed and developing countries. Businesses from all industries experienced economic anxiety and could not achieve their business goals. This project attempted to fill this gap by assessing the perceptions of SMEs in Kuwait toward the economic anxiety and by assessing their efforts to reach their targets during the different stages of the lockdowns in Kuwait. The findings showed several problems in performing well due to the challenges and problems caused by the COVID-19 pandemic. The report provided a set of recommendations that can help improve SMEs’ performance in Kuwait to enable them to overcome the crisis. Yet, several issues remain open for further investigation, such as developing a framework for implementing the potential solutions.