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on Small Business Management |
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Issue of 2026–05–04
fourteen papers chosen by João Carlos Correia Leitão, Universidade da Beira Interior |
| By: | Felipe Aguilar; Roberto Alvarez |
| Abstract: | In this paper, we use data for more than 5, 000 Chilean companies to investigate whether participation in business association increases the probability of R&D investment. Dealing with the endogeneity of participation through a bivariate Probit model with an exclusion variable that captures the trust environment among firms, we find that this probability increases by about 27%. This effect is heterogeneous across firms. Participation increases the probability of R&D investment by 30.8% for SMEs and by 43.9% for those companies with severe financial constraints. Our evidence is consistent with the idea that associativity may help SMEs to close the innovation gap and/or to alleviate financial problems. |
| Date: | 2025–12 |
| URL: | https://d.repec.org/n?u=RePEc:chb:bcchwp:1064 |
| By: | Díaz Cachay, Pedro Antonio |
| Abstract: | Family businesses have a prominent presence around the world, constituting 70 – 90% of global firms (Zellweger, 2017). These businesses play an important role in regional economies and employment rates (Shepherd and Zacharakis, 2000). This article explores how exit intentions among small family businesses vary across the North Central Region (NCR) using data from the NCR-STAT: Small Business Survey (Wiatt et al., 2024). The Small Business Survey contains 1, 287 responses from small business owners; however, our analysis focuses only on respondents who identified their firm as a family business, resulting in 494 observations. Chua et al. (1999) describe family businesses as firms in which a defining feature is the intention to transfer ownership and control to the next generation within the family. In this study, survey respondents self-identified whether they considered their small business to be a family business. Our focus is the North Central Region, which consists of the states of Illinois, Indiana, Iowa, Kansas, Michigan, Minnesota, Missouri, Nebraska, North Dakota, Ohio, South Dakota, and Wisconsin. Understanding where family businesses are located across the region is an important first step in analyzing their transition decisions. However, location alone does not determine whether these firms are prepared for the future. In the next section, we examine how succession planning varies across family businesses in the North Central Region as an indicator of readiness to transfer the firm to the next generation. |
| Keywords: | Agribusiness, Community/Rural/Urban Development |
| Date: | 2026–04–28 |
| URL: | https://d.repec.org/n?u=RePEc:ags:ncrcrd:397836 |
| By: | Giuseppe Pulito; Mariola Pytlikova; Sarah Schroeder; Magnus Lodefalk |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer– employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technologyspecific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:cer:papers:wp818 |
| By: | Giuseppe Pulito; Mariola Pytlikova; Sarah Schroeder; Magnus Lodefalk |
| Abstract: | Using two waves of nationally representative Danish firm surveys linked to employer-employee administrative registers, we study how adoption varies across artificial intelligence (AI) and related advanced technologies. We show that AI adoption is highly technology-specific. While firm size and digital infrastructure predict adoption broadly, workforce composition operates through distinct channels: STEM-educated workforces predict core AI adoption, whereas non-STEM university-educated workforces are associated with generative AI adoption, indicating different human capital complementarities. The factors associated with adoption differ from those predicting deployment breadth: firm size and digital maturity matter for both, whereas workforce composition primarily predicts adoption alone. Machine learning and natural language processing are deployed across multiple business functions, whereas other advanced technologies remain concentrated in specific operational domains. Individual-level evidence provides a foundation for these patterns, with awareness of workplace AI usage concentrated among managers and high-skilled workers. Self-reported AI knowledge is higher among younger and more educated individuals. Finally, commonly used occupational AI exposure measures vary substantially in their ability to predict observed adoption, with benchmark-based measures outperforming patent-based and LLM-focused alternatives. These findings show that treating AI as a monolithic category obscures economically meaningful variation in who adopts, what they deploy, and how well existing measures capture it. |
| Keywords: | Artificial Intelligence; Technology Adoption; Digitalisation; Human capital; AI Exposure Measures |
| JEL: | D24 J23 J62 O33 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26089 |
| By: | Schilirò, Daniele |
| Abstract: | This paper offers a conceptual review of the relationship between the knowledge economy and innovation, challenging simplistic, linear assumptions about how new ideas are generated. Given their increasing global significance, the study focuses on Fourth Industrial Revolution (4IR) technologies. Specifically it examines the nature, evolution, and defining features of the knowledge economy. Such an economy relies on increasing specialization, research, innovation, and continuous learning, with learning and experience being its most critical sources. Furthermore, the paper argues that innovation constitutes a fundamental dimension of the knowledge economy, noting that knowledge production is strictly related to innovations. Rather than a sequential chain, innovation is presented here as a complex, systemic process characterized by multiple feedbacks and loops. This analysis highlights the systemic, non-deterministic nature of innovation and its strong relationship with knowledge. However, the capacity of companies to innovate depends heavily on the innovation ecosystem—the framework where stakeholders interact and collaborate—as well as the regulatory and legislative framework. Consequently, several factors, including the availability of sufficient human capital with appropriate education and advanced skills, the presence of robust infrastructure, and the role of institutions, are necessary to make companies' innovations effective. While this paper does not claim to provide definitive answers, it seeks to offer new insights for future research. |
| Keywords: | knowledge economy; knowledge; learning; networks; innovation; technological progress; competitiveness |
| JEL: | D83 L10 O30 O32 |
| Date: | 2025 |
| URL: | https://d.repec.org/n?u=RePEc:pra:mprapa:128049 |
| By: | Paolo Pinotti; Gianmarco Daniele; Marco De Simoni; Domenico Marchetti; Giovanna Marcolongo |
| Abstract: | We show that credit constraints significantly increase the risk that firms are infiltrated by organized crime, defined as the covert involvement of criminal organizations in corporate decision-making. Using confidential data on criminal investigations, credit ratings, and loan histories for the universe of Italian firms, we find that a downgrade to substandard credit status reduces credit availability by 30% over five years and increases the probability of infiltration by 5%, relative to comparable firms. A local randomization design comparing firms just above and below the downgrade threshold confirms this result. The effect is pervasive across sectors and regions, but particularly strong in real estate, where the probability of infiltration rises by 10% following a downgrade. Infiltrated firms also display higher survival rates than other downgraded firms, despite similar declines in employment and revenues. These findings suggest that organized crime can serve as a financial backstop -- sustaining non-viable businesses and potentially redirecting their strategies to serve criminal interests. |
| Keywords: | Organized crime, Firms, Bank Credit |
| JEL: | G32 K42 L25 O17 |
| Date: | 2025–11 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:25101 |
| By: | Cheruiyot, Josea K. |
| Abstract: | This paper examines the incidence and determinants of credit rationing and borrower discouragement among Kenyan SMEs using firmlevel data from the 2018 World Bank Enterprise Survey. Only about onequarter of firms report no financing obstacles, while the majority face constraints of varying severity. Younger, informally registered, femaleowned, and unaudited firms are significantly more likely to be constrained, consistent with informational opacity and limited collateral. Credit application patterns indicate extensive selfexclusion: roughly threequarters of SMEs do not apply for loans despite plausible financing needs, citing anticipated rejection, high interest rates, collateral requirements, or other perceived deterrents. Among those who apply, approval rates exceed 90 percent, suggesting that effective rationing arises mainly from preapplication barriers rather than lender denial. These findings indicate that frictions-limited transparency, weak disclosure, and elevated borrower risk perceptions-play a central role in suppressing SME participation in the formal credit market. Policies that expand collateral substitutes, strengthen credit information systems, and support financial reporting could alleviate these frictions and broaden access to credit. |
| Keywords: | SMEs, Credit, Financing Constraints, Discouraged Borrowers, Kenya |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kbawps:340183 |
| By: | Wairimu, Salome; Kiragu, Doris |
| Abstract: | Micro, small and medium enterprises (MSMEs) are critical drivers towards the growth of the economy and contribute largely towards poverty eradication and employment. In Kenya, the MSME sector contributes to 40% of GDP, and employs approximately 14.9 million. Yet, this sector continues to experience difficulties in accessing credit from financial institutions. MSMEs' find it difficult to access credit from banks while banks find it difficult to lend to MSMEs due to lack of visibility. Additionally, reliance on informal credit sources in unsustainable. MSME borrowers are often trapped in vicious cycles of indebtedness and predatory lenders hampering firms' long-term performance. As a result, this study seeks to examine the key constraints hindering MSMEs' access to formal financing and their contribution to the financing gap. Similarly, to evaluate policy interventions that can enhance credit availability and bridge the MSME financing gap. The study adopted a logit regression estimation model, with the average marginal effects used to interpret the findings. The results indicate that lenders perceive MSME borrowers as risky and use credit measures like collateral to mitigate these risks. Factors like lack of collateral, guarantors, negative CRB listing exclude MSMEs from access to credit. Therefore, there is need for policies aimed at supporting banks in extending credit to MSMEs' as well as building capacity among the MSMEs in Kenya for their growth and sustainability |
| Keywords: | Capital, Competition, Stability, Panel |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kbawps:340181 |
| By: | Jennie Bai; Luc Laeven; Yaojun Ke; Hong Ru |
| Abstract: | We study the global footprint and real effects of Chinese overseas corporate ownership. By assembling a comprehensive micro-level dataset of 161, 773 firms across 159 countries (2012–2021), we independently reconstruct multi-layered ownership chains to trace capital through offshore tax havens to its ultimate origin. This approach reveals a global footprint substantially broader than official FDI statistics. Chinese-controlled foreign assets expanded at 20% annually, reaching $2.1 trillion or roughly 3% of global corporate assets by 2021. Chinese investors—particularly state-owned enterprises (SOEs)—strategically target R&D-intensive and supply-chain-linked firms. Following acquisition, target firms increase capital stock and R&D expenditures, yet these inputs fail to generate higher patent output and are accompanied by a significant decline in profitability. We document a novel 'innovation spillback' mechanism: while target innovation remains stagnant, Chinese parent firms experience a sharp acceleration in granted patents following their first developed-economy acquisition. Furthermore, a greater Chinese presence crowds out R&D at non-target peer firms, though aggregate industry-level innovation remains unchanged. China thus represents a distinctively state-driven model of global ownership that accepts weaker near-term performance to internalize technological capacity at home. |
| JEL: | F3 G32 G34 O3 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:nbr:nberwo:35106 |
| By: | Calvert Jump, Robert; Scavette, Adam |
| Abstract: | No abstract available. |
| Keywords: | regional business cycles; research universities; regional resilience |
| Date: | 2024–05–09 |
| URL: | https://d.repec.org/n?u=RePEc:gpe:wpaper:47059 |
| By: | Ndwiga, David |
| Abstract: | The paper examines the effects of commercial banks' adoption of Environmental, Social and Governance practices on Micro, Small and Medium-sized Enterprises support in Kenya. The study is underpinned on the growing demand for sustainable financing by entreprises in the wake of need for sustainable businesses. The study focused on commercial banks that have adopted ESG practices and report their sustainability progress. Using panel data analysis study found that environmental, social and governance practices integration significantly increases the number of Micro, Small and Medium-sized Enterprises trained. Conversely, banks' ESG adoption was found to positively but insignificantly affect MSME lending. The results conclude that banks' training to MSMEs is necessary but not sufficient for increased MSME lending. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kbawps:340179 |
| By: | Muli, Anthony; Ndwiga, David; Agung, Raphael; Njoroge, Samantha |
| Abstract: | The study seeks to examine the effects of credit guarantee schemes on Micro, Small and Medium-sized Enterprises (MSME) lending. Specifically, the paper examined the performance of a specified credit guarantee scheme, effect of the credit guarantee scheme on MSME loan default probability and how loan repayment performs across different sectors. The study utilised bank level data for 2, 398 MSMEs under the specified credit guarantee scheme. The study found that the credit guarantee scheme improves MSMEs access to formal credit. Furthermore, low default rate was reported based on firm count. Regarding credit guarantee coverage ratio - default probability nexus, this study established existence of moral hazard effect which is statistically significant. However, across different sectors, short run models find credit guarantee moral hazard sectoral bias, largely elevated in agriculture, building and construction, trade and manufacturing sectors. Additionally, default risk was found to reduce with firm age. |
| Date: | 2026 |
| URL: | https://d.repec.org/n?u=RePEc:zbw:kbawps:340176 |
| By: | Martina Bossard, Marc Möller, Catherine Roux |
| Abstract: | We use a stylized model of a dynamic innovation tournament to show that the effectiveness of monetary incentives depends on whether contestants receive cardinal, ordinal, or no information about their rival’s performance. The model’s main implication is that performance information acts as a substitute for prize money in creating incentives to invest in new ideas: The investment-maximizing information policy switches from no to ordinal to cardinal information as the tournament’s prize is reduced. A laboratory experiment provides support for our theory but also unveils an unpredicted pattern of behavior capable of overturning the model’s conclusions concerning optimal policy. |
| Keywords: | Innovation Tournaments; Performance Information; Rank Information; R&D Investment. |
| JEL: | O31 C72 D83 |
| Date: | 2026–03 |
| URL: | https://d.repec.org/n?u=RePEc:ube:dpvwib:dp2602 |
| By: | Francesca Miserocchi; Savannah Noray; Alice Wu |
| Abstract: | The advances of artificial intelligence (AI) are built on the groundwork laid by researchers. We study the labor market competition between academia and industry for AI researchers and its consequences for public knowledge production. Using data on 150, 000 computer science researchers, we document a major reallocation of AI talent toward top technology firms between 2005 and 2020. Publications at AI conferences predict transitions to top firms more strongly than to academia. Exploiting acceptance decisions at a leading AI conference, we compare accepted authors with similar rejected authors and find that a publication increases the probability of moving to a top firm by 2-6 percentage points in the next 1-3 years. Sorting to top firms is stronger for male researchers, whereas female students and postdocs are more likely to get tenure-track positions following a publication. Researchers who move to top firms subsequently publish fewer papers, resulting in approximately 1, 000 fewer AI papers and 2, 000 fewer papers in other computer science areas per year in the public domain. |
| Keywords: | Sorting, Productivity Signals, Labor Market Concentration, Innovation |
| JEL: | J23 J24 O31 |
| Date: | 2026–04 |
| URL: | https://d.repec.org/n?u=RePEc:crm:wpaper:26106 |