US Small Business Research Articles

Small Business Disaster Recovery in the United States

Natural disasters pose significant challenges to small businesses, as they are more vulnerable to shocks. Their recovery is crucial to the resilience of communities. This literature review explores the topic of US small business disaster recovery by examining research in economics, management, and disaster studies. The review highlights a consensus among small business researchers that even within the small business sector, size matters. There is an understanding that space and time play a crucial role not only in the recovery process itself but also in how researchers may need to measure recovery. A common thread is that the recovery process may not have a prescriptive endpoint, and that the endpoint to recovery may be in the eye of the beholder. The review exposes significant gaps in the literature, particularly regarding long-term recovery processes, standardized measurement metrics, and the complicated dynamics between business and household recovery.

Leveraging predictive analytics and Machine learning to optimize US Small Business resilience and Economic Growth

Small businesses are the backbone of the U.S. economy, contributing significantly to job creation, innovation, and economic growth. However, they face persistent challenges, including market volatility, financial instability, and operational inefficiencies, which can hinder their long-term sustainability. This study explores the role of predictive analytics and machine learning in enhancing small business resilience and optimizing economic growth. Predictive analytics, powered by machine learning algorithms, enables businesses to analyze historical data, identify patterns, and generate insights that improve decision-making. This research examines how small businesses can leverage predictive models to forecast consumer demand, optimize supply chains, and manage financial risks. Small enterprises can proactively address market fluctuations, prevent revenue losses, and improve resource allocation by utilizing time-series forecasting, sentiment analysis, and anomaly detection. Additionally, machine learningdriven automation enhances operational efficiency by streamlining business processes, reducing human errors, and improving customer experiences. The study also highlights real-world case studies where predictive analytics has successfully helped small businesses navigate economic uncertainties. By adopting data-driven strategies, entrepreneurs can enhance their adaptability to economic disruptions, improve profitability, and sustain longterm growth. Moreover, the findings underscore the importance of integrating AI-driven insights into policymaking to support small businesses and strengthen the overall economy.

Which Nursing Homes Received Paycheck Protection Program Funding?

Abstract BackgroundNearly 90% of nursing homes in the US experienced severe staffing shortages during COVID‐19, which disproportionately affected over 50% of nursing home residents living with Alzheimer’s Disease and Related Dementia (ADRD). One major federal policy to support the maintenance of staffing levels during the COVID‐19 pandemic was the Paycheck Protection Program (PPP), a $75 billion program that provided loans to small businesses, including nursing homes. Loans were forgivable if at least 60 percent of the loan was spent on payroll providing an incentive to apply resources toward staffing. This paper examines the organizational and staffing characteristics of nursing homes that received PPP loans, including the prevalence of residents living with ADRD and Serious Mental Illness (SMI).MethodWe examine a national cohort of non‐VA nursing homes eligible for PPP funding. We compare those who received a PPP loan to nursing homes that did not receive a PPP loan. PPP loan receipt was captured in the US Small Business Administration (SBA) PPP data from April 2020 to June 2021. We primarily use datasets from 2019 to examine baseline characteristics, including Nursing Home Compare data, the Minimum Dataset (MDS), LTC Focus, and the Payroll‐Based Journal data.Result3,536 nursing homes received a PPP loan in any of the three rounds; 9,943 eligible nursing homes never received a PPP loan.More than 50% of nursing home residents were living with ADRD, and nearly 1 in 3 had a serious mental illness. These rates were slightly higher among facilities receiving PPP loans.Staffing in 2019 was inferior in nursing homes receiving PPP loans than those who did not on all measures: lower‐star ratings on both quality and staffing measures, higher turnover rates at the RN (9.7% vs. 9.0%), LPN (11.2% vs. 10.3%), and CNA levels (11.4% vs. 11.0%). COVID‐19 affected PPP‐receiving nursing homes more in terms of confirmed cases (522 vs. 511), resident deaths (107 vs. 97), and staffing shortages (22% vs. 18%).ConclusionThis first‐ever direct federal funding effort to support nursing home staff during a pandemic appears to have supported the most vulnerable nursing homes.

The Causal Effects of R&D Grants: Evidence from a Regression Discontinuity

Abstract We leverage the discontinuity in the assignment mechanism of the Small and Medium Enterprise Instrument—the first European research and development (R&D) subsidy targeting small firms—to provide the broadest quasi-experimental evidence on R&D grants over both geographical and sectoral scopes. Grants trigger sizable impacts on a wide range of firm-level outcomes. Heterogeneous effects are consistent with grants reducing financial frictions. This reduction is due to funding rather than certification. We also provide direct causal evidence on pure certification—signaling not attached to funding—and show that firms that only receive “quality stamps” do not improve their performance. Finally, our estimates suggest that the scheme produces private returns that are positive and comparable to those of the U.S. Small Business Innovation Research program, while also generating geographical and sectoral spillovers in the form of increased rates of entrepreneurial entry.

Competitive funding and academic-industry collaboration: policy trends and insights

Abstract In an era of globalized research endeavors, the interplay between government funding programs, funding decisions, and their influence on successful research collaborations and grant application success rates has emerged as a critical focus of inquiry. This study embarks on an in-depth analysis of cross-country funding dynamics over the past three decades, with a specific emphasis on support for academic-industry collaboration versus sole academic or industry funding. Drawing insights from comprehensive datasets and policy trends, our research illuminates the evolving landscape of research funding and collaboration policies. We examine funding by Innosuisse (Swiss Innovation Project Funding) and SBIR (US Small Business Innovation Research), exploring the rates of future grant success for both academic and industry partners. We find strong evidence of rich-get-richer phenomenon in the Innosuisse program for both academic partners and industry partners in terms of winning future grants. For SBIR we find weaker levels of continued funding to the same partners with most attaining at most a few grants. With the increasing prevalence of academic-industry collaborations among both funders, it is worth considering additional efforts to ensure that novel ideas and new individuals and teams are supported.

"Adapting Small-Scale Fiber Production to the 21st Century: Maine’s Evolving Strategies"

This study, funded by the US Small Business Administration, aims to better understand the operational, strategic, and economic dimensions of Maine’s fiber producers’ businesses. Fiber producers are those raising animal-based fiber, including sheep, alpacas, rabbits, and goats. The producer focus of this paper highlights the responses from the 27 fiber producers and their insights into the realities of Maine’s fiber sector.

Can data save small businesses? Benefits and challenges of data analytics adoption among small-sized retailers

Small businesses were recently hit hard by the COVID-19 pandemic, and there has been a crucial need for them to meet changing consumer behaviour and industry trends through data analytics. The purpose of this study is to understand the status of the adoption and utilisation of data analytics among small-sized retailers. Through in-depth interviews with US small business owners in the retail industry, the findings provide an overview of data analytics implementation status among small businesses. With regard to technological factors, the advantages of data analytics utilisation were identified (ie a better understanding of customers, strategic decision making, optimisation of marketing tools and accurate sales/trends forecast). Technological difficulties in understanding data and data analytics tools appeared as challenges. With regard to organisational factors, the small size of the firm and top management support were identified to be influential factors in the data analytics adoption process. However, lack of time, training resources and human resources were identified as major organisational challenges. As for environmental factors, macroeconomic contexts such as the COVID-19 pandemic and dynamic market trends influenced small businesses to adopt data analytics. Based on the technology-organisation-environment framework, the findings contribute to SME research and become a stepping stone to deriving a new framework.

Assessing the Loss Given Default of Bank Loans Using the Hybrid Algorithms Multi-Stage Model

The loss given default (LGD) is an important credit risk parameter in the regulatory system for financial institutions. Due to the complex structure of the LGD distribution, we propose a new approach, called the hybrid algorithms multi-stage (HMS) model, to construct a multi-stage LGD prediction model and test it on the US Small Business Administration (SBA)’s small business credit dataset. We then compare the model’s performance under four routes by different evaluation metrics. Finally, pertinent business information and macroeconomic features datasets are added for robustness validation. The results show that HMS performs well and stably for predicting LGD, confirming the superiority of the proposed hybrid unsupervised and supervised machine learning algorithm. Financial institutions can apply the approach to make default predictions based on other credit datasets.

Small business administration loans, economic development, and state‐level employment

Small business administration loans, economic development, and state‐level employment

Commentary: Thinking of moving from academia into industry? Here are some things to consider

Commentary: Thinking of moving from academia into industry? Here are some things to consider

An assessment of the US Small Business Innovation Research (SBIR) program: A study of project failure

Abstract In 2000 and 2012, the US Congress charged the National Research Council (NRC) to study how the Small Business Innovation Research (SBIR) program had stimulated technological innovation and used small businesses to meet Federal research and development needs and to recommend program improvements. Using project data collected by the NRC, we suggest that an important assessment metric not previously considered by the NRC in its reports to Congress relates to the failure rate of funded Phase II research projects. We identify a number of covariates associated with project failure, and we recommend that program managers might decrease the likelihood of project failure if funded firms can be given relevant information about how to contact angel investors, venture capitalists, private investors, and the like, and how to present a proposal for additional research investment dollars. Our findings should have a direct benefit to other countries that have implemented SBIR-like programs.

Enterprise Financial Risk Analysis Based on Improved Model C-Means Clustering Algorithm

As a provider of loans to SMEs, banks should prudently examine loan risks while ensuring that they provide loans to SMEs from the perspective of cooperating with policy implementation and controlling their own risks. The existing loan risk measurement tools include multiple discriminant analysis models, multiple regression models, and machine learning methods. Most machine learning methods have higher prediction accuracy than traditional models when using historical data for calculation, but the existence of problems such as overfitting seriously affects the robustness of machine learning methods. A similar method is introduced into the loan default risk prediction of SMEs, and the mean clustering method is used to preset penalty items to reduce overfitting and high accuracy to help banks effectively identify the default probability of SMEs during the loan period. This study will use the mean clustering method to iteratively train 900,000 SME credit records published by the US Small and Medium Business Administration, with 27 dimensions of data provided by Small Business Administration (SBA) to provide partial guarantees. A regression tree evaluates the data, combining the scores of multiple regression trees to produce a final prediction of the probability of credit default on the input data. The research results show that the mean clustering method can effectively improve the prediction accuracy of traditional machine learning methods and multiple linear regression in the scenario of SME loan default prediction and reduce the overfitting and black-box properties. As a supplementary loan default risk measurement tool, it can strengthen the ability of commercial banks to control the risk of loan business and can also promote the development of small- and medium-sized enterprises and the market economy to a certain extent.

Supporting Agri-Food SMEs in Italy in the Post-COVID-19 Context: From Horizon 2020 to Horizon Europe

In recent decades, European policy has targeted specific measures towards SMEs. The recently concluded Horizon 2020 Programme, under the Europe 2020 Strategy, has provided economic support to stimulate competitiveness and boost the economy of member states by focusing on innovation. The SME Instrument, through a structure of multiple calls, constituted the vehicle for SMEs to access the funds made available by the European Union with disruptive innovation proposals. The initial structure of the SME Instrument, developed in phases and along the lines of the US Small Business Innovative Research (SBIR) Programme, was afterwards simplified in the design of its successor, the EIC Accelerator Pilot. The success of the above mentioned economic instruments led the European institutions to develop a new instrument, the EIC Accelerator, supported by the current Horizon Europe programme. After outlining the main features of this transition and the novelties introduced, this paper aims to ascertain the extent to which SMEs in the agri-food sector in Italy have had the ability and opportunity to intercept funds by measuring participation in the SME Instrument. Furthermore, it intends to verify which type of firms in the agri-food sector, divided into agricultural and industrial enterprises, intercepted more of the examined funds, and which organisational phase, productive or commercial, the enterprises have been oriented towards, also giving relevancy to the specific objects of the winning projects.

Bank switching of US small businesses: new methods and evidence

Despite being informationally opaque, small firms often switch from their primary financial institution to transactional lenders, with the relationship banking theory invoking the holdup problem as a culprit explanation. Using US evidence and an estimation strategy that overcomes traditional shortcomings in small business research, our study captures the determinants and, for the first time, the ex post effects of the switching decision. We find that switching is less likely when the primary financial institution is a nearby bank associated with quality services and connected to the firm via other business or social relationships. Small firms become more loyal as they grow in size and pursue nonmortgage credit. Outside the primary relationship, both loan approval and borrowing cost are adversely impacted, however loan maturities are longer. Moreover, the likelihood of pledging collateral remains unaffected, provided that the type of collateral is least sensitive to the borrower’s information environment. Jointly, our findings describe a trade-off inconsistent with the holdup problem, and an opportunity for banks to enhance customer loyalty by improving aspects of the relationship unrelated to the terms of credit.

Debunking Lien Myths: Empirical Evidence for an Essential Tool in the Fight against Wage Theft

Wage lien laws have immense potential to help workers collect owed wages. Because liens can secure rights to property before full adjudication, workers can rest assured that real assets will exist should they prevail and scofflaw employers cannot easily hide assets from collections. Despite the proven usefulness of wage lien laws, opponents frequently argue that broad lien regimes would restrict credit.We use a difference in differences regression analysis to test this argument, using data from the US Small Business Administration 7(a) loan program. We look to two multistate metropolitan areas in the United States with divided wage lien regimes – Chicago and Washington DC. In each case, wage lien laws are permitted in one state (Wisconsin and Maryland, respectively) and not allowed in others (Illinois and Indiana for Chicago, Virginia for Washington).We test the argument against wage liens through regression analysis, looking at gross approvals and interest rates as a reflection of risk. We also aggregate observations by 4-digit NAICS code, by lending institution, and by ZIP postal code and reran regression analyses. The wage lien law treatment did not lower the gross approval amount of 7(a) loans in either market, and did not increase the interest rate faced by borrowers. Indeed, results, while mostly statistically insignificant, in some cases showed the opposite effect. Although these tests do not definitively prove that wage lien laws do not constrict credit flows to small businesses, they provide absolutely no evidence to support such an argument either. The findings thus support the position of advocates supporting wage lien laws as a common-sense tool in the arsenal against wage theft, while casting substantial doubt on oppositional claims against wage liens.AbstractThis article evaluates the impact on business access to credit of wage lien laws, which are an important tool for low-wage workers to reduce wage theft. Worker advocates have tried to expand access to wage liens. Opponents argue that wage liens negatively impact business access to credit. Using data from the Small Business Administration 7(a) loan program, one of the only detailed sources of business loan data, we assess whether lien laws affect business access to credit in two states—Wisconsin and Maryland—using a difference-in-differences framework. We find no evidence to support wage lien opponents’ claims that lien laws reduce credit access. The findings contribute to the understanding of wage liens and provide evidence in support of policies that protect workers.

Fraudulent loans and the United States paycheck protection program

PurposeDespite lessons learned from prior disaster relief funding programs, billions of dollars in fraudulent loans were issued by the Paycheck Protection Program (PPP) during the COVID-19 pandemic in the USA. The misuse of funds prevented business owners and their employees who are in true financial need from accessing program funds. The purpose of this paper is to identify techniques perpetrators used to obtain funds from the program illegally since its inception in March 2020 and concludes with suggestions on internal controls to reduce fraud occurrences in future relief packages.Design/methodology/approachThe authors analyze 106 loan fraud cases reported by the US Department of Justice and compiled by the Project on Government Oversight to examine methods individuals used to illegally obtain funds from the program. The authors complement the data with lender characteristics from Call Reports and Business Insights. They further compare the fraud sample to the entire population of PPP loans, which is available on the US Small Business Administration website. The authors report descriptive statistics, correlations and multivariate regressions.FindingsThe authors find that most fraud cases falsify tax data to access program loans and inflate payroll numbers to obtain larger loan amounts. Applicants who sought large amounts applied using multiple companies and across multiple lenders, consistent with the use of multiple loans to avoid the scrutiny of a single large loan with a single lender. The authors find that cases with larger amounts relied on less regulated lenders, such as lending companies, rather than more regulated lenders.Originality/valueThe PPP is part of the largest ever US stimulus in which the private sector allocated funds. This study provides novel evidence of how fraudsters adapted to the program's rules to defraud the government.

Strategic Adaptive Leadership and Emerging Approaches to Online Marketing of a US Small Business Real Estate Firm in Response to COVID-19

The COVID-19 pandemic has changed the business dynamics for many organizations globally. This paper examines the demonstration of adaptable strategic leadership and marketing towards online and digital marketing approaches of a newly developed waterfront high rise condominium complex. As a result of COVID-19, business sales projections are off by 85%. This paper explores organizational development intervention practitioner-based applied qualitative action research that makes recommendations on the most resourceful options in which small businesses can attract customers and boost sales even with the pandemic. For this case study, the name Moreso, a fictitious name, will be used to respect the organization that provided the data used and approved the ability to publish these findings' privacy and intellectual capital. This research approach's value in this instance is built on the reality that many similar small businesses in the real estate industry experience these same dynamics because of COVID-19 but have no starting framework on how to respond.

Advancing US small business apparel production: a state-level mixed-method exploration

PurposeThis study highlights the impact of attaining and incorporating knowledge and building relationships with other firms in US apparel production.Design/methodology/approachProducers of apparel and sewn products operating within a US state form the sample for this mixed methods exploration. Study 1 involves a qualitative analysis of the producers highlighting knowledge in interorganizational discussions. Study 2 quantitatively measures responses from 38 producing firms. Ordinary least squares (OLS) regression was used to measure associations, and simple slopes were computed to examine interactions.FindingsThe knowledge exchanged, according to participants in study 1, was limited, thus warranting further examination. Results from study 2 revealed strong associations among knowledge absorptive capacity, social interaction and people-oriented culture with network ties. A two-way interaction effect was found for absorptive capacity and social interaction, indicating association between social interaction and network ties was more positive at higher levels of absorption capacity and vice versa.Research limitations/implicationsFindings provide theoretical and applied support for building network ties. This research operationalizes complicated-to-measure constructs critical to the empirical measurement of junctions from two theoretical frameworks, in the context of a specific industry. Though acceptable for exploratory research, additional work is needed to refine reliability measures and to examine a national sample.Originality/valueThis study links elements of social capital- and knowledge-based views of the firm necessary for reviving US apparel production. Broader outcomes from this research include job creation providing support and growth in the US sewn apparel and goods industry that will drive US economy.

Social support as a driver of social entrepreneurial intentions: the moderating roles of entrepreneurial education and proximity to the US small business administration

PurposeThe purpose of this paper is two-fold: to examine the relationship between social support and social entrepreneurial intentions and to test the moderating roles of entrepreneurial education and physical proximity to the office of the US Small Business Administration.Design/methodology/approachThe study adopts a quantitative methodological approach. The hypotheses are tested on a sample of 1,245 respondents who intend to start a business in the state of Florida. Factor analysis and multiple regression analysis were used to identify the relationship between social support and social entrepreneurial intentions.FindingsThe hypotheses are supported by the results. The study found a positive and significant relation between social support and social entrepreneurial intentions. It also establishes the moderating effects of entrepreneurial education and proximity to office of the US Small Business Administration on the relationship between social support and social entrepreneurial intentions, i.e. our results show not only that higher levels of social support are associated with higher entrepreneurial intentions but that this association becomes stronger with entrepreneurial education and proximity to the office of the US Small Business Administration.Originality/valueThis is the first empirical study that highlights the role of entrepreneurial education and physical proximity to the US Small Business Administration in moderating the relations between social support and social entrepreneurial intentions. The study contributes to the understanding of factors that influence social entrepreneurial intentions.

A financial performance-based assessment of SMEs’ competitiveness – an analysis of Hungarian and US small businesses

The study aims to identify the financial performance measures used as a proxy of the firm-level competitiveness dimensions of small and medium-sized enterprises and their competitiveness. By investigating the factors that affect competitiveness in general, those areas will be introduced, related to an identified competitiveness dimension. Financial and non-financial performance indicators will assess these areas. The paper considers competitiveness as an outcome variable, suggests a relationship between financial performance and the identified areas, and searches for the financial performance measures drivers. A panel data model was tested on Hungarian small and medium-sized enterprises (SMEs) and US SMEs. The collected data cover the period between 2013 and 2017. As a result of the applied panel regression, those variables were successfully identified that drive and could predict financial performance measures related to competitiveness. The research found a significant difference between the two-sample dataset results, which differences can be connected to country, industry, and, in general, to economic development characteristics.The results provide decision-making support and hint about the managerial tools and techniques aiming to control the firm characteristics, performance, and, eventually, firm-level competitiveness. Based on the results, further research can be dedicated to the development characteristics of firm-level competitiveness and the analysis of the relationship between the competitiveness dimensions and competitiveness itself. AcknowledgmentThe research was financed by the Higher Education Institutional Excellence Programme of the Ministry for Innovation and Technology in Hungary, within the framework of the 4th thematic programme „Enhancing the Role of Domestic Companies in the Reindustrialization of Hungary” of the University of Pécs.