Performance implications of technological uncertainty, age, and size for small businesses

ABSTRACT

Small businesses face multiple sources of uncertainty that can hold negative implications for performance by magnifying liabilities of newness and/or exacerbating resource inadequacies. However, technological uncertainty may hold positive implications for performance by creating opportunities that small businesses are better enabled to exploit. These implications are largely unexamined in prior research, particularly for smaller/micro businesses. We theorize and examine these implications and find positive performance effects from technological uncertainty that are greater for younger and smaller businesses and robust among the smallest “micro” businesses. Results suggest that higher levels of technological uncertainty may create conditions for performance advantages for small businesses, but these advantages dissipate among older/larger businesses, holding interesting implications for research and practice.

KEYWORDS:

• Uncertainty
• liability of newness
• liability of smallness
• entrepreneurial flexibility

Notes

1 Two Kauffman Foundation-published studies are particularly relevant: From 1980 to 2005, firms less than 5 years old accounted for all net job growth in the United States (Haltiwanger et al., 2009), and young firms (excluding startups) accounted for nearly two-thirds of job creation in 2007 (Stangler & Litan, 2009).

2 Note that commonly accepted definitions in the EU, based on OECD standards, vary slightly from these U.S.-based classifications and define micro businesses as one with fewer than 10 employees (OECD, 2005). Similarly, Eurostat defines a micro business as one with nine employees or less, while most research in the U.S. has used 20 or 25 as the upper limit (Alsaaty et al., 2016; Heshmati, 2001; Mittelstaedt et al., 2003).

3 We thank an anonymous reviewer for identifying this important relationship.

4 One could also include a fourth category, Region-Specific Uncertainty. In a study of four U.S. recessions, Owyang et al. (2005) identified states that were in sync with the national pattern and others that tended to be out of sync. While some external uncertainty will vary based on geographic region, regional variation is also tied to industry that often may be correlated with region—for example, autos, tech companies, etc.

5 Prior research has emphasized the similarities (Kuratko, 2006; Mullen et al., 2009) or differences (Stewart et al., 1999) between the leaders of various types of firms, such as entrepreneurial founders of potential high-growth firms or owners of older small firms with little or no growth. For simplicity’s sake, we refer to all these leaders as managers, although our hypothesis development distinguishes between the effects on young or small firms.

6 From a measurement standpoint, one might question how technological turbulence could increase the relative performance of all firms. However, for the reasons stated, during a period of high uncertainty one might expect that the performance of small firms (as a class) would improve while that of larger incumbents would be static or decline.

7 Some research has drawn a distinction between liability of newness and liability adolescence, arguing that the mechanism and empirical predictions of Stinchcombe apply to brand new organizations, while a different mechanism explains possible death years later (Abatecola et al., 2012; Fichman & Levinthal, 1991). However, Henderson (1999) demonstrated similar effects across both time periods rather than distinct effects for each period, so here we consider adolescence and newness as a single theoretical mechanism.

8 Freel (2005) represents macro, industry, and firm uncertainty as a nested hierarchy, which captures the independence of these levels of uncertainty but does not suggest a measurement model.

9 http://www.usinflationcalculator.com/inflation/current-inflation-rates/.

10 Note that the U.S. Small Business Administration (SBA) differs somewhat from OECD in that it utilizes classification criteria of ownership structure, revenue, and number of employees for classifying the size of a business (USSBA, 2020). In classifying by number of employees, the SBA size definition is 500 or fewer employees as defined in the Electronic Code of Federal Regulations part 121-Small Business Size Regulations (e-CFR, 2020).

11 Following recent Bureau of Labor Statistics guidelines for classifying high-technology NAICS codes (Hecker, 2005), we identified 17 firms in NAICS code 51xx and 85 in code 54xx, for a total of 13.5 percent of our sample. Excluding these firms from the analysis resulted in the same sign and comparable magnitude and significance for all parameters that were found significant in Models 3, 4 and 5. This observation suggests that our results regarding technological uncertainty are due to the broader population of medium- and low-tech firms rather than the smaller fraction of 97 firms whose industries are driven by technology-based competitive advantage.