Abstract
Extensive research has suggested that new ventures’ growth rates lack persistence. However, existing studies tend not to differentiate between positive and negative growth rates or examine their respective persistence and dynamics. Building on performance feedback theory, we show that positive and negative growth rates exhibit different dynamics over time. Especially, negative growth rates are less likely to persist and more likely to reverse than positive ones. Furthermore, firm size and team size influence the persistence—or the lack of thereof—of both positive and negative growth rates. Specifically, new ventures that are smaller, as well as those managed by larger teams, are more likely to maintain positive growth and reverse negative growth, relative to their respective counterparts. Overall, this study suggests that positive and negative growth rates differ in their persistence and that new ventures can shape the dynamics of both positive and negative growth.
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Notes
Performance feedback theory suggests that firms have both historical and social aspirations. Historical aspirations arise from past performance, while social aspirations arise from peer performance. In this study, we focus solely on historical aspirations for two primary reasons. First, we are interested primarily in the dynamic relationships between past growth rates and future growth rates, and such relationships do not involve social aspirations. Second, social aspirations tend to be less well-defined for new ventures, as social aspirations often depend on the particular reference groups that new ventures compare themselves with.
Ye et al. (2020) examine profit persistence, showing that firms’ profitability growth tends to lack persistence over time, while firms’ profitability relative to their peers tends to be quite persistent over time. Also, Ye et al. (2020) confirm that positive and negative profitability changes (i.e., profitability growth and decline) have important implications for organizational search. Nevertheless, Ye et al. (2020) do not examine whether profitability growth and decline differ in their persistence or whether such differences have important implications for subsequent firm performance.
Performance relative to historical aspiration is usually referred to as historical performance feedback. Since we focus only on historical performance feedback, we may refer to it as performance feedback for short in this paper.
Our final sample excludes observations that do not have sales revenue and those with missing values on the studied variables. Among the firms that have completed the Kauffman Firm Survey, 2 years of data (8926 observation) are lost due to the lag structure of the model, 2888 observations are excluded due to no or missing sales revenue, 5446 observations are further excluded due to missing values on other studied variables, and 665 observations are excluded due to singletons. To assess the potential impact of missing values, we have carried out an additional analysis, in which we use multiple imputation to impute the missing values and then use the imputed values to estimate our dynamic panel data models. The results from this additional analysis are largely consistent with those from our main analyses.
As Greve (2008) suggests, accounting for time-invariant firm heterogeneity is critical for research on firm growth, because unobserved fixed factors may affect the varying growth performance across firms. For example, founding conditions may affect growth rates (Barron et al. 1994; Cooper et al. 1994), so do many other time-invariant factors (Gilbert et al. 2006). Accounting for time-invariant firm heterogeneity allows us to focus on the impacts of time-varying factors on organizational growth. Accounting for time-specific effects is also critical to this study for two major reasons. First, firms experienced changing macroeconomic conditions during the study period. Time-specific effects account for the impacts of macroeconomic conditions on new venture growth. Second, because all firms in this dataset were founded in 2004, time is related to firm age. As a result, time-specific effects also account for the effect of age and aging on organizational growth.
We thank the anonymous reviewers for identifying the different roles of historical and social aspirations in new ventures and for suggesting a focus on historical aspiration. Nevertheless, we have carried out an additional analysis, in which we include the positive and negative splines of social performance feedback into the models. Specifically, we consider new ventures in the same 4-digit NAICS industry as peers. We then use mean sales revenue as social aspiration and split social performance feedback into positive and negative splines. Overall, the results from this analysis are consistent with the results from our main analyses.
We have tried to model social aspiration in a preliminary analysis and include social performance feedback into our models. Specifically, we include new ventures in the same 4-digit NAICS industry as social peers, consider their mean sales revenue as social aspiration, and split social performance feedback into positive and negative splines. We find that the results from this analysis are largely consistent with the results from our main analyses.
References
Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Review of Economic Studies, 58(2), 277–297. https://doi.org/10.2307/2297968.
Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51. https://doi.org/10.1016/0304-4076(94)01642-D.
Arend, R. J., & Chen, Y. (2012). Entrepreneurship as dynamic, complex, disequilibrious: a focus that benefits strategic organization. Strategic Organization, 10(1), 85–95. https://doi.org/10.1177/1476127011431340.
Audia, P. G., & Greve, H. R. (2006). Less likely to fail: low performance, firm size, and factory expansion in the shipbuilding industry. Management Science, 52(1), 83–94. https://doi.org/10.2307/20110485.
Barron, D. N., West, E., & Hannan, M. T. (1994). A time to grow and a time to die: Growth and mortality of credit unions in New York city, 1914-1990. American Journal of Sociology, 100(2), 381–421. https://doi.org/10.2307/2782074.
Bianchini, S., Bottazzi, G., & Tamagni, F. (2017). What does (not) characterize persistent corporate high-growth? Small Business Economics, 48(3), 633–656. https://doi.org/10.1007/s11187-016-9790-1.
Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143. https://doi.org/10.1016/S0304-4076(98)00009-8.
Bond, S. R. (2002). Dynamic panel data models: a guide to micro data methods and practice. Portuguese Economic Journal, 1(2), 141–162. https://doi.org/10.1007/s10258-002-0009-9.
Bond, S. R., Hoeffler, A., & Temple, J. R. (2001). GMM estimation of empirical growth models. CEPR Discussion Paper No. 3048.
Bottazzi, G., Cefis, E., & Dosi, G. (2002). Corporate growth and industrial structures: some evidence from the Italian manufacturing industry. Industrial and Corporate Change, 11(4), 705–723. https://doi.org/10.1093/icc/11.4.705.
Bradley, S. W., Wiklund, J., & Shepherd, D. A. (2011). Swinging a double-edged sword: the effect of slack on entrepreneurial management and growth. Journal of Business Venturing, 26(5), 537–554. https://doi.org/10.1016/j.jbusvent.2010.03.002.
Bromiley, P. (1991). Testing a causal model of corporate risk taking and performance. Academy of Management Journal, 34(1), 37–59. https://doi.org/10.2307/256301.
Bromiley, P., & Harris, J. D. (2014). A comparison of alternative measures of organizational aspirations. Strategic Management Journal, 35(3), 338–357. https://doi.org/10.1002/smj.2191.
Bruno, G. S. F. (2005). Approximating the bias of the LSDV estimator for dynamic unbalanced panel data models. Economics Letters, 87(3), 361–366. https://doi.org/10.1016/j.econlet.2005.01.005.
Cameron, A. C., & Trivedi, P. K. (2005). Microeconometrics: methods and applications. New York: Cambridge University Press.
Capasso, M., Cefis, E., & Frenken, K. (2014). On the existence of persistently outperforming firms. Industrial and Corporate Change, 23(4), 997–1036. https://doi.org/10.1093/icc/dtt034.
Cassar, G. (2014). Industry and startup experience on entrepreneur forecast performance in new firms. Journal of Business Venturing, 29(1), 137–151. https://doi.org/10.1016/j.jbusvent.2012.10.002.
Chan, L. K. C., Karceski, J., & Lakonishok, J. (2003). The level and persistence of growth rates. Journal of Finance, 58(2), 643–684. https://doi.org/10.1111/1540-6261.00540.
Chen, Y. (2018). Partial adjustment toward target R&D intensity. R&D Management, 48(5), 591–602. https://doi.org/10.1111/radm.12320
Chen, M.-J., & Hambrick, D. C. (1995). Speed, stealth, and selective attack: How small firms differ from large firms in competitive behavior. Academy of Management Journal, 38(2), 453–482. https://doi.org/10.5465/256688.
Coad, A. (2007). A closer look at serial growth rate correlation. Review of Industrial Organization, 31(1), 69–82. https://doi.org/10.1007/s11151-007-9135-y.
Coad, A. (2009). The growth of firms: a survey of theories and empirical evidence. Cheltenham: Edward Elgar Publishing.
Coad, A., & Hölzl, W. (2009). On the autocorrelation of growth rates. Journal of Industry, Competition and Trade, 9(2), 139–166. https://doi.org/10.1007/s10842-009-0048-3.
Coad, A., & Rao, R. (2008). Innovation and firm growth in high-tech sectors: a quantile regression approach. Research Policy, 37(4), 633–648. https://doi.org/10.1016/j.respol.2008.01.003.
Coad, A., Frankish, J., Roberts, R. G., & Storey, D. J. (2013). Growth paths and survival chances: An application of Gambler’s Ruin theory. Journal of Business Venturing, 28(5), 615–632. https://doi.org/10.1016/j.jbusvent.2012.06.002.
Coad, A., Frankish, J. S., Roberts, R. G., & Storey, D. J. (2016). Predicting new venture survival and growth: does the fog lift? Small Business Economics, 47(1), 217–241. https://doi.org/10.1007/s11187-016-9713-1.
Coad, A., Daunfeldt, S.-O., & Halvarsson, D. (2018). Bursting into life: firm growth and growth persistence by age. Small Business Economics, 50(1), 55–75. https://doi.org/10.1007/s11187-017-9872-8.
Cooper, A. C., Gimeno-Gascon, F. J., & Woo, C. Y. (1994). Initial human and financial capital as predictors of new venture performance. Journal of Business Venturing, 9(5), 371–395. https://doi.org/10.1016/0883-9026(94)90013-2.
Corner, P. D., Kinicki, A. J., & Keats, B. W. (1994). Integrating organizational and individual information processing perspectives on choice. Organization Science, 5(3), 294–308. https://doi.org/10.2307/2635132.
Cotei, C., & Farhat, J. (2018). The M&A exit outcomes of new, young firms. Small Business Economics, 50(3), 545–567. https://doi.org/10.1007/s11187-017-9907-1.
Crawford, G. C., Aguinis, H., Lichtenstein, B., Davidsson, P., & McKelvey, B. (2015). Power law distributions in entrepreneurship: Implications for theory and research. Journal of Business Venturing, 30(5), 696–713. https://doi.org/10.1016/j.jbusvent.2015.01.001.
Cyert, R. M., & March, J. G. (1992). A behavioral theory of the firm (2nd ed.). Malden: Blackwell Publishers.
Daunfeldt, S.-O., & Elert, N. (2013). When is Gibrat’s law a law? Small Business Economics, 41(1), 133–147. https://doi.org/10.1007/s11187-011-9404-x.
Daunfeldt, S.-O., & Halvarsson, D. (2015). Are high-growth firms one-hit wonders? Evidence from Sweden. Small Business Economics, 44(2), 361–383. https://doi.org/10.1007/s11187-014-9599-8.
Davidsson, P., Steffens, P., & Fitzsimmons, J. (2009). Growing profitable or growing from profits: putting the horse in front of the cart? Journal of Business Venturing, 24(4), 388–406. https://doi.org/10.1016/j.jbusvent.2008.04.003.
Desai, V. M. (2008). Constrained growth: how experience, legitimacy, and age influence risk taking in organizations. Organization Science, 19(4), 594–608. https://doi.org/10.1287/orsc.1070.0335.
Durlauf, S. N., Johnson, P. A., & Temple, J. R. W. (2005). Growth econometrics. In A. Philippe & N. D. Steven (Eds.), Handbook of economic growth (Vol. Volume 1, Part A) (pp. 555–677). Amsterdam: Elsevier.
Eisenhardt, K. M., & Schoonhoven, C. B. (1990). Organizational growth: linking founding team, strategy, environment, and growth among US semiconductor ventures, 1978-1988. Administrative Science Quarterly, 35(3), 504–529. https://doi.org/10.2307/2393315.
Farhat, J., & Robb, A. (2018). Analyzing complex survey data: the Kauffman Firm Survey. Small Business Economics, 50(3), 657–670. https://doi.org/10.1007/s11187-017-9913-3.
Farhat, J., Matusik, S., Robb, A., & Robinson, D. T. (2018). New directions in entrepreneurship research with the Kauffman Firm Survey. Small Business Economics, 50(3), 521–532. https://doi.org/10.1007/s11187-017-9905-3.
Flannery, M. J., & Hankins, K. W. (2013). Estimating dynamic panel models in corporate finance. Journal of Corporate Finance, 19, 1–19. https://doi.org/10.1016/j.jcorpfin.2012.09.004.
Gaba, V., & Joseph, J. (2013). Corporate structure and performance feedback: aspirations and adaptation in M-form firms. Organization Science, 24(4), 1102–1119. https://doi.org/10.1287/orsc.1120.0788.
Gavetti, G., Greve, H. R., Levinthal, D. A., & Ocasio, W. (2012). The behavioral theory of the firm: assessment and prospects. Academy of Management Annals, 6(1), 1–40. https://doi.org/10.1080/19416520.2012.656841.
Gilbert, B. A., McDougall, P. P., & Audretsch, D. B. (2006). New venture growth: a review and extension. Journal of Management, 32(6), 926–950. https://doi.org/10.1177/0149206306293860.
Goddard, J., Wilson, J., & Blandon, P. (2002). Panel tests of Gibrat’s law for Japanese manufacturing. International Journal of Industrial Organization, 20(3), 415–433. https://doi.org/10.1016/S0167-7187(00)00085-0.
Greve, H. R. (1998). Performance, aspirations, and risky organizational change. Administrative Science Quarterly, 43(1), 58–86. https://doi.org/10.2307/2393591.
Greve, H. R. (2003). Organizational learning from performance feedback: a behavioral perspective on innovation and change. Cambridge: Cambridge University Press.
Greve, H. R. (2007). Exploration and exploitation in product innovation. Industrial and Corporate Change, 16(5), 945–975. https://doi.org/10.1093/icc/dtm013.
Greve, H. R. (2008). A behavioral theory of firm growth: sequential attention to size and performance goals. Academy of Management Journal, 51(3), 476–494. https://doi.org/10.5465/AMJ.2008.32625975.
Hahn, J., Hausman, J., & Kuersteiner, G. (2007). Long difference instrumental variables estimation for dynamic panel models with fixed effects. Journal of Econometrics, 140(2), 574–617. https://doi.org/10.1016/j.jeconom.2006.07.005.
Haleblian, J., & Finkelstein, S. (1993). Top management team size, CEO dominance, and firm performance: the moderating roles of environmental turbulence and discretion. Academy of Management Journal, 36(4), 844–863. https://doi.org/10.2307/256761.
Hall, B. H., Mairesse, J., & Mohnen, P. (2010). Measuring the returns to R&D. In H. H. Bronwyn & R. Nathan (Eds.), Handbook of the economics of innovation (Vol. 2, pp. 1033–1082). Amsterdam: North-Holland.
Hambrick, D. C., & D’Aveni, R. A. (1992). Top team deterioration as part of the downward spiral of large corporate bankruptcies. Management Science, 38(10), 1445–1466. https://doi.org/10.2307/2632673.
Hansen, L. P. (1982). Large sample properties of generalized method of moments estimators. Econometrica, 50(4), 1029–1054. https://doi.org/10.2307/1912775.
Hinsz, V. B., Tindale, R. S., & Vollrath, D. A. (1997). The emerging conceptualization of groups as information processors. Psychological Bulletin, 121(1), 43–64. https://doi.org/10.1037/0033-2909.121.1.43.
Holtz-Eakin, D., Newey, W., & Rosen, H. S. (1988). Estimating vector autoregressions with panel data. Econometrica, 56(6), 1371–1395. https://doi.org/10.2307/1913103.
Hölzl, W. (2014). Persistence, survival, and growth: a closer look at 20 years of fast-growing firms in Austria. Industrial and Corporate Change, 23(1), 199–231. https://doi.org/10.1093/icc/dtt054.
Hoskisson, R. E., Chirico, F., Zyung, J., & Gambeta, E. (2017). Managerial risk taking: a multitheoretical review and future research agenda. Journal of Management, 43(1), 137–169. https://doi.org/10.1177/0149206316671583.
Huang, R., & Ritter, J. R. (2009). Testing theories of capital structure and estimating the speed of adjustment. Journal of Financial and Quantitative Analysis, 44(02), 237–271.
Jones, A. M., Rice, N., d’Uva, T. B., & Balia, S. (2013). Applied health economics (2nd ed.). New York: Routledge.
Jordan, A. H., & Audia, P. G. (2012). Self-enhancement and learning from performance feedback. Academy of Management Review, 37(2), 211–231. https://doi.org/10.5465/amr.2010.0108.
Josefy, M., Kuban, S., Ireland, R. D., & Hitt, M. A. (2015). All things great and small: organizational size, boundaries of the firm, and a changing environment. Academy of Management Annals, 9(1), 715–802. https://doi.org/10.1080/19416520.2015.1027086.
Keele, L., & Kelly, N. J. (2006). Dynamic models for dynamic theories: the ins and outs of lagged dependent variables. Political Analysis, 14(2), 186–205. https://doi.org/10.1093/pan/mpj006.
Kennan, J. (1979). The estimation of partial adjustment models with rational expectations. Econometrica, 47(6), 1441–1455. https://doi.org/10.2307/1914011.
Kiviet, J. F. (1995). On bias, inconsistency, and efficiency of various estimators in dynamic panel data models. Journal of Econometrics, 68(1), 53–78. https://doi.org/10.1016/0304-4076(94)01643-E.
Klotz, A. C., Hmieleski, K. M., Bradley, B. H., & Busenitz, L. W. (2014). New venture teams: a review of the literature and roadmap for future research. Journal of Management, 40(1), 226–255. https://doi.org/10.1177/0149206313493325.
Kogan, L., Papanikolaou, D., Seru, A., & Stoffman, N. (2017). Technological innovation, resource allocation, and growth. Quarterly Journal of Economics, 132(2), 665–712. https://doi.org/10.1093/qje/qjw040.
Kotiloglu, S., Chen, Y., & Lechler, T. (2019). Organizational responses to performance feedback: a meta-analytic review. Strategic Organization. https://doi.org/10.1177/1476127019883361
Lakonishok, J., Shleifer, A., & Vishny, R. W. (1994). Contrarian investment, extrapolation, and risk. Journal of Finance, 49(5), 1541–1578. https://doi.org/10.1111/j.1540-6261.1994.tb04772.x.
Lang, L., Ofek, E., & Stulz, R. (1996). Leverage, investment, and firm growth. Journal of Financial Economics, 40(1), 3–29. https://doi.org/10.1016/0304-405X(95)00842-3.
Lim, E. N. K., & McCann, B. T. (2014). Performance feedback and firm risk taking: the moderating effects of CEO and outside director stock options. Organization Science, 25(1), 262–282. https://doi.org/10.1287/orsc.2013.0830.
March, J. G. (2010). The ambiguities of experience. Ithaca: Cornell University Press.
March, J. G., & Shapira, Z. (1987). Managerial perspectives on risk and risk taking. Management Science, 33(11), 1404–1418. https://doi.org/10.1287/mnsc.33.11.1404.
McKinley, W., Latham, S., & Braun, M. (2014). Organizational decline and innovation: turnarounds and downward spirals. Academy of Management Review, 39(1), 88–110. https://doi.org/10.5465/amr.2011.0356.
Miller, D., & Friesen, P. H. (1983). Successful and unsuccessful phases of the corporate life cycle. Organization Studies, 4(4), 339–356. https://doi.org/10.1177/017084068300400403.
Mishina, Y., Pollock, T. G., & Porac, J. F. (2004). Are more resources always better for growth? resource stickiness in market and product expansion. Strategic Management Journal, 25(12), 1179–1197. https://doi.org/10.1002/smj.424.
Moschella, D., Tamagni, F., & Yu, X. (2018). Persistent high-growth firms in China’s manufacturing. Small Business Economics. https://doi.org/10.1007/s11187-017-9973-4.
Nickell, S. (1981). Biases in dynamic models with fixed effects. Econometrica, 49(6), 1417–1426. https://doi.org/10.2307/1911408.
Oliveira, B., & Fortunato, A. (2006). Firm growth and liquidity constraints: a dynamic analysis. Small Business Economics, 27(2), 139–156. https://doi.org/10.1007/s11187-006-0006-y.
Posen, H. E., Keil, T., Kim, S., & Meissner, F. (2018). Renewing research on problemistic search—a review and research agenda. Academy of Management Annals, 12(1), 208–251.
Robb, A. M., & Watson, J. (2012). Gender differences in firm performance: evidence from new ventures in the United States. Journal of Business Venturing, 27(5), 544–558. https://doi.org/10.1016/j.jbusvent.2011.10.002.
Roodman, D. (2009). How to do xtabond2: an introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.
Shapira, Z. (1995). Risk taking: a managerial perspective. New York: Russell Sage Foundation.
Shaver, J. M., & Mezias, J. M. (2009). Diseconomies of managing in acquisitions: evidence from civil lawsuits. Organization Science, 20(1), 206–222. https://doi.org/10.1287/orsc.1080.0378.
Shinkle, G. A. (2012). Organizational aspirations, reference points, and goals: building on the past and aiming for the future. Journal of Management, 38(1), 415–455. https://doi.org/10.1177/0149206311419856.
Singh, J., & Fleming, L. (2010). Lone inventors as sources of breakthroughs: myth or reality? Management Science, 56(1), 41–56. https://doi.org/10.1287/mnsc.1090.1072.
Solon, G., Haider, S. J., & Wooldridge, J. M. (2015). What are we weighting for? Journal of Human Resources, 50(2), 301–316. https://doi.org/10.3368/jhr.50.2.301.
Stam, E., & Wennberg, K. (2009). The roles of R&D in new firm growth. Small Business Economics, 33(1), 77–89. https://doi.org/10.1007/s11187-009-9183-9.
Steffens, P., Davidsson, P., & Fitzsimmons, J. (2009). Performance configurations over time: Implications for growth– and profit–oriented strategies. Entrepreneurship Theory and Practice, 33(1), 125–148. https://doi.org/10.1111/j.1540-6520.2008.00283.x.
Sutton, J. (1997). Gibrat’s legacy. Journal of Economic Literature, 35(1), 40–59. https://doi.org/10.2307/2729692.
Sutton, R. I., & Hargadon, A. (1996). Brainstorming groups in context: effectiveness in a product design firm. Administrative Science Quarterly, 41(4), 685–718. https://doi.org/10.2307/2393872.
Tornqvist, L., Vartia, P., & Vartia, Y. O. (1985). How should relative changes be measured? American Statistician, 39(1), 43–46. https://doi.org/10.1080/00031305.1985.10479385.
Verver, H., van Zelst, M., Lucas, G. J. M., & Meeus, M. (2019). Understanding heterogeneity in the performance feedback–organizational responsiveness relationship: a meta-analysis. https://osf.io/hq4uw.
Vissa, B., Greve, H. R., & Chen, W.-R. (2010). Business group affiliation and firm search behavior in India: responsiveness and focus of attention. Organization Science, 21(3), 696–712. https://doi.org/10.1287/orsc.1090.0475.
Wennberg, K., Delmar, F., & McKelvie, A. (2016). Variable risk preferences in new firm growth and survival. Journal of Business Venturing, 31(4), 408–427. https://doi.org/10.1016/j.jbusvent.2016.05.001.
Wiklund, J., & Shepherd, D. A. (2011). Where to from here? EO-as-experimentation, failure, and distribution of outcomes. Entrepreneurship Theory and Practice, 35(5), 925–946. https://doi.org/10.1111/j.1540-6520.2011.00454.x.
Wintoki, M. B., Linck, J. S., & Netter, J. M. (2012). Endogeneity and the dynamics of internal corporate governance. Journal of Financial Economics, 105(3), 581–606. https://doi.org/10.1016/j.jfineco.2012.03.005.
Wooldridge, J. M. (2001). Applications of generalized method of moments estimation. Journal of Economic Perspectives, 15(4), 87–100. https://doi.org/10.1257/jep.15.4.87.
Wuchty, S., Jones, B. F., & Uzzi, B. (2007). The increasing dominance of teams in production of knowledge. Science, 316(5827), 1036–1039. https://doi.org/10.2307/20036287.
Ye, Y., Yu, W., & Nason, R. (2020). Performance feedback persistence: comparative effects of historical versus peer performance feedback on innovative search. Journal of Management. https://doi.org/10.1177/0149206320916225.
Acknowledgments
The corresponding author is Michael Song (drmichaelsong@163.com). We thank the associate editor, Diemo Urbig, and three anonymous reviewers for their helpful comments and suggestions. We thank the Ewing Marion Kauffman Foundation for providing access to the restricted-access data file of the Kauffman Firm Survey. Any opinions, findings, and conclusions or recommendations expressed in this material are those of the authors and do not necessarily reflect the views of the Ewing Marion Kauffman Foundation.
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Chen, Y., Song, M. The persistence and dynamics of new venture growth. Small Bus Econ 58, 303–322 (2022). https://doi.org/10.1007/s11187-020-00411-2
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DOI: https://doi.org/10.1007/s11187-020-00411-2