Here, even if rivals are aware and motivated to match the firm’s investments, they do not necessarily have access to the requisite resources to do so. This, in part, explains why it is especially difficult for firms experiencing poor performance to turn around their fortunes (Morrow et al., 2007); instead they often become less aggressive and slower to act (Ferrier et al., 2002). These arguments suggest that higher prior performance provides firms the opportunity to enrich their strength set as well as reduce their set of weaknesses over time.
Hypothesis 4b (H4b). Higher prior performance leads to an increase in firms’ strength sets over time.
Hypothesis 4c (H4c). Higher prior performance leads to a decrease in firms’ weakness sets over time.
The data presented in this study were collected by the Banque de France[4] as part of its Sesame project. The Sesame project was a nonprofit venture developed to collect detailed strategic and managerial data on French industrial firms to complement the financial data that the Banque de France already possesses. A random sample of 4,169 small and medium sized industrial firms was selected to participate in the survey (Cool and Henderson, 1998). Because of the sample’s size, each year econometricians from the Banque de France administered the survey to one-third of the sample, from 2001 to 2003. Thus, the data are not panelized. The interviews were conducted by Banque de France agents specially trained in survey techniques; they conducted personal interviews with CEOs using a computer-aided questionnaire. The businesses were classified according to the European equivalent of the SIC classification system (the NACE) at the three-digit level. We analyzed those firms that also had complete financial performance data, resulting in a final sample of 2,980 firms belonging to 78 separate industries. The average number of firms per three-digit industry is 38.21 and the average size of these firms is 104 employees (please see Appendix 1). This sample was used to test the performance hypotheses.
From 2004 to 2006, the Banque de France administered a second survey to the initial group of firms. This survey was identical to the initial instrument. In total, 1,868 firms from the first round participated in the second survey. Missing data limited the set of usable observations to 1,578. This sample was used to test hypotheses related to changes in strength and weakness sets.
Dependent variables
Three separate dependent variables were used to test the hypotheses. To assess the performance hypotheses, an accounting-based measure was used because many firms in the sample are private for which no market-based data are available. Specifically, we measured performance with firm value-added. This figure approximates the contribution the firm has made in the transformation process from inputs to finished output. It measures the full value actually created by the firm through its operations before its distribution to the firm’s stakeholders (e.g., family members and
employees). This measure corresponds to the total economic value created by the capital and labor employed by a firm and is a fundamental performance metric (Lieberman and Dhawan, 2005). Value-added is calculated by subtracting from the firm’s total revenue any intermediate consumption, which includes such items as raw materials, semifinished products, external services used to manufacture finished products, energy consumption, etc. Also, considering the presence of many private firms in the sample, in which owners may try to limit their declared net profit with tax strategies (George, 2005), value-added is a more accurate proxy for performance because it is less likely to reflect such biases. As such, this type of measure is an appropriate metric for empirical studies of the RBV, especially when private firms are in the sample. Also, because it is not a ratio, there are no concerns regarding the source of its variance.
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