The Valuation of Management Control Systems in Start-Up Companies: International Field-Based Evidence

Abstract

The question of whether management control systems (MCSs) adopted by start-up companies are valuable is examined. We investigate an international sample of start-ups, including their detailed MCS adoptions and financing histories. We find that higher MCS intensity, which is measured as the number of systems adopted at year-end immediately prior to the financing round, has a positive impact on company value. We also find that equity financiers value MCS more than do debt financiers. The valuation implication is more pronounced for start-up companies operating in highly competitive environments and with higher growth. We also document a positive relation between change in MCS intensity and change in firm value. Additional analyses show that higher company valuation is found for companies that align their MCS choices with their strategic positioning. In particular, systems that implement strategy are perceived to be more important and valuable than others. Overall, our paper provides new evidence for the debate concerning the merits of formal control in start-up companies.

Acknowledgements

We thank an anonymous reviewer, A. Rashad Abdel-khalik, Agyenim Boateng, Jon Davis, Peter Fiechter, Nen-Chen (Richard) Hwang, Kentaro Koga, Laurence van Lent (editor), Suresh Radhakrishnan, Michael Seamer, and participants at the 2013 International Journal of Accounting Symposium for helpful comments.

Notes

1 We define MCS as ‘formal, information-based routines and procedures managers use to maintain or alter patterns in organizational activities’ (Simons, 1995, p. 5) that focus on the financial and non-financial aspects of the company. Thus, we interpret them as recurring and formalised sets of institutionalised protocols and routines designed to motivate, monitor, and measure the behaviour of managers and employees, as well as assist them in information gathering and decision-making.

2 The concept of a new venture has no common definition in the existing entrepreneurship literature. Aldrich, Kalleberg, Marsden, and Cassell (1989) identify three possibilities in defining a new business: an enterprise that was formed when all the elements of the business were assembled for the first time into one coherent entity, a takeover by a new owner, and any change in the legal business form. We consider companies created via the first path as a new venture.

3 A recent World Economic Forum report finds that early-stage companies in different countries face similar management challenges, even if the underlying ecosystems differ greatly (see Foster et al., 2014).

4 In cases in which discrepancies occurred, we contacted the company CEO and CFO to validate the information.

5 Information on the formalisation of MCS is self-reported by survey respondents. To ensure a consensus understanding of what ‘formalisation’ means, we provided the following definition of a ‘formal system’ in the surveys: ‘A formal system is one that has a documented process and/or periodically and purposefully executes on that process.’ Many surveys in this study were conducted in person by the authors, who explained ‘formal system’ to the survey respondents.

6 In the questionnaires, we asked respondents to answer the following two questions: (1) ‘Where was the company established (city or state/country)?’ and (2) ‘Where is the company legally incorporated (city or state/country)?’ In most cases, the two answers indicated the same country, but in some cases (e.g. several Chinese companies in our sample), the company was established in the People's Republic of China but was legally incorporated in the Cayman Islands or British Virgin Islands. Since the country of legal incorporation is typically chosen for financing or tax reasons, we based our country classification on the location of the company's main business operation.

7 VentureOne provides the industry classification scheme.

8 In the CFO questionnaire, we asked respondents to provide the year and month of each financing event. We did not ask for the precise date, because financing events typically take several months. Therefore, interpolation is done on a monthly basis, as opposed to a daily basis.

9 As a robustness check, we repeated all empirical analyses using rank regression. Rank regression has been shown to perform well when the dependent variable is a non-linear, monotonic function of the independent variables (Armstrong et al., 2006). This is likely to be the case for the start-up companies that comprise our sample. Indeed, Sahlman (1993) documents three option components of venture capital investment: the option to abandon investment, the option to revalue a project, and the option to increase capital commitment. These options are likely to introduce non-linearities in the valuation of private equity securities. Further, in contrast to robust regression techniques, such as winsorising extreme observations, rank regression gives equal weight to extreme observations (Iman & Conover, 1979). Thus, extreme observations of either the dependent or independent variables are not treated as ‘outliers’ but instead as valid data points that are accorded equal weight in the analysis. Empirical findings remain qualitatively similar under this model specification, although generally weaker. The coefficient estimate on Ln(MCS) is 0.260 under the matching approach and 0.178 under the interpolation approach, and both are significant at the 10% level.

10 As a robustness check, we used internal company employees only in our main analyses, and the inferences remained unchanged.

11 As venture capital financing constitutes 55% of our sample, we also examined the valuation of MCS using a venture capital-backed subsample only. We still documented a positive relation between MCS intensity and firm's private-market valuation.

12 We also examined whether MCS intensity is associated with the choice between equity and debt financing. In particular, we estimated a probit model whether the dependent variable equals 1 if the firm obtains equity capital for a given financing round and 0 otherwise. In addition to the key variable of interest Ln(MCS), we included a set of control variables that prior studies found to affect firm's financing choice: firm size as proxied by revenue and headcount, profitability, growth, R&D intensity, dummy for early round, and high-tech. The coefficient estimate on Ln(MCS) is negative but insignificant.

13 There is no overlap between the two data sets.

14 As a robustness test, we used an alternative measure of competition based on market concentration. Specifically, we asked survey respondents to report the percentage of market share held by their top four domestic and top four international competitors. High concentration is seen as a signal of weak competition. As such, Competition equals 1 if the top eight competitors hold less than 50% of the market share and 0 otherwise. Empirical findings remain qualitatively unchanged.

15 As an alternative specification, we examine Global as a single contingent factor in the regression and find that the coefficient Ln(MCS)×Global remains insignificant under both matching and interpolation valuation approaches.

16 Results remain qualitatively similar if we use latest product line instead, although with a reduced sample size, since some companies only had one product line.

17 We obtained qualitatively similar findings, using either the full sample or the 92% subsample of firms with a successful match rate.

18 We exclude PriorCEOexp and industry and country dummies from the regressions, as they are time-invariant variables and thus the coefficients cannot be estimated.

19 PriorCEOExp is excluded from the model, as it is a constant variable across time.

Additional information

Funding

This work was supported by the National Natural Science Foundation of China [grant numbers 71002013 and 71372049].