Firm capabilities and growth: the moderating role of market conditions

Abstract

Using a contingency theory lens, this study explores the impact of multiple firm-level capabilities and their interactions on firm growth under different market conditions, using panel data from 612 U.S. public firms across 16 years in 60 industries. Specifically, this study empirically examines how three key firm capabilities (marketing, R&D, operations) interact to impact firms’ revenue growth and profit growth over time, and how external boundary conditions (market munificence and competitive dynamism) influence the interactive growth effects of these capabilities. The results indicate that firms’ R&D (operations) capabilities positively (negatively) influence the effects of marketing capabilities on firm growth and that such effects vary across different market conditions. This study provides insights to researchers and managers regarding how to manage and deploy resources across multiple capabilities simultaneously under different market conditions to drive firm growth.

Appendices

Appendix 1

Table 6 SIC industries included in sample

Appendix 2

Stochastic frontier estimation of firm capabilities

Firm capabilities are estimated based on a general least squares random-effects model and stochastic frontier (SF) model, following Kumbhakar et al. (2015). Accordingly, firm capabilities are modeled via a persistent component, which is firm-specific time-invariant, and a residual component, which is firm- and time-specific. This approach allows us to separate firm capabilities into persistent and time-varying components (Kumbhakar et al., pp. 274–278).

$$ \mathrm{The}\kern0.5em \mathrm{general}\kern0.5em \mathrm{S}\mathrm{F}\kern0.5em \mathrm{function}\kern0.5em \mathrm{is}:{\mathrm{Output}}_{it}={\alpha}_0+{\alpha}_1\times inpu{t}_{1 it}+{\alpha}_2\times inpu{t}_{2 it}+\dots +{\mu}_i+{\varepsilon}_{it} $$

Where μ _i is a firm-level unobserved random effect and ε _it is a firm and time specific error term. We further decomposed μ _i to estimate the firm-specific time-invariant persistent component and ε _it to estimate the firm- and time-specific component. Conceptually, both μ _i and ε _it are to be interpreted as inefficiency scores, capturing a firm’s inefficiency in converting resources (i.e., inputs) into the output.

The firm-specific time-invariant persistent component and the firm-specific time-variant residual component are obtained based on the maximum likelihood estimates of the following equations:

$$ {\mu}_i = {\vartheta}_1\hbox{--} {\delta}_i+{\nu}_{i \operatorname {}}{\varepsilon}_{it}={\vartheta}_1\hbox{--} {\gamma}_{it}+{\omega}_{it} $$

The firm-specific time-invariant persistent capability is Exp(−δ _i ), and the firm-specific time-variant residual capability is Exp(−γ _it ). The overall firm capabilities are the product of persistent and residual capabilities (see Kumbhakar et al., chapter 10). This general method accommodates for several distributional assumptions on these error terms. Following Dutta et al. (1999, 2005), we assume μ _i  ~ N(0, σ _μ ²), ε _it  ~ N(ε, σ _ε ²) with ε > 0, E[μ _i ε _it ] = 0.

Specifically, to estimate marketing capabilities, for firm i in year t in each industry, we estimate:

$$ \begin{array}{l} ln\left({\mathrm{Sales}}_{it}\right)={\alpha}_0+{\alpha}_1\cdot ln\left({\mathrm{AD}}_{it}\right)+{\alpha}_2\cdot ln\left({\mathrm{AD}}_{i\left(t-1\right)}\right)+{\alpha}_3\cdot ln\left({\mathrm{SGA}}_{it}\right) + {\alpha}_4\cdot ln\left({\mathrm{SGA}}_{i\left(t-1\right)}\right)+{\alpha}_5\cdot ln\left({\mathrm{TRM}}_{it}\right)+\hfill \\ {}\kern6em +{\alpha}_6\cdot {\mathrm{IND}}_i + {\mu}_i + {\varepsilon}_{it}\hfill \end{array} $$

where, μ _i and ε _it are as described above and used to compute marketing capabilities;

AD _it :

= advertising expenses of firm i in year t;

AD _i(t-1) :

= advertising expenses of firm i in year t-1;

SGA _it :

= SG&A expenses of firm i in year t;

SGA _i(t-1) :

= SG&A expenses of firm i in year t-1;

TRM _it :

= number of trademarks of firm i in year t;

IND _i :

= industry dummies (2-digit SIC code of firm i), and

Sales _it :

= sales revenue of firm i in year t

Similarly, to estimate R&D capabilities, for firm i in year t in each industry, we estimate:

$$ ln\left({\mathrm{PTS}}_{it}\right)={\beta}_0+{\beta}_1\cdot ln\left({\mathrm{RD}}_{it}\right)+{\beta}_2\cdot ln\left({\mathrm{RD}}_{i\left(t-1\right)}\right)+{\beta}_3\cdot ln\left({\mathrm{PTS}}_{i\left(t-1\right)}\right)+{\beta}_4\cdot {\mathrm{IND}}_i+{\mu}_i+{\varepsilon}_{it} $$

where, μ _i and ε _it are as described above and used to compute R&D capabilities;

PTS _it :

= number of patents of firm i in year t

RD _it :

= R&D expenses of firm i in year t

RD _i(t-1) :

= R&D expenses of firm i in year t-1

PTS _i(t-1) :

= patent stock of firm i in year t-1

IND _i :

= industry dummies (2-digit SIC code of firm i), and

Finally, to estimate operations capability, for firm i in year t in each industry, we estimate:

$$ ln\left({\mathrm{COGS}}_{it}\right)={\gamma}_0+{\gamma}_1\cdot ln\left({\mathrm{XPR}}_{it}\right)+{\gamma}_2\cdot ln\left({\mathrm{XCAP}}_{it}\right)+{\gamma}_3\cdot {\mathrm{IND}}_i+{\mu}_i+{\varepsilon}_{it} $$

where, μ _i and ε _it are as described above and used to compute operations capabilities;

COGS _it :

= cost of goods of firm i in year t

XPR _it :

= labor costs of firm i in year t

XCAP _it :

= capital costs of firm i in year t, capital costs are total interest and dividends paid

IND _i :

= industry dummies (2-digit SIC code of firm i).

All estimated firm capabilities are rescaled as a 1–100 indexes (Bahadir et al. 2008).