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.

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Fig. 1

Notes

  1. 1.

    We tested our models on revenue and profit levels as well as growth and found a similar pattern of results. However, as the focus of this paper is on firm growth, we do not include these additional results in the paper.

  2. 2.

    Such reductions in the number of observations are common when several secondary sources are merged. However, excluding these 388 firms due to missing data, etc. does not affect the generalizability of our sample. A two-sample mean difference t-test shows that the missing firms are not statistically different from the remaining sample in terms of total assets, number of employees, firm age, ROA, and sales volume. Moreover, a two-stage Heckman sample selection model further confirms that there is no selection bias in our sample due to missing data.

  3. 3.

    We used labor costs and capital costs where both data were available; where labor costs were not available, we used capital costs only. The correlations between the measures including/not including labor costs is 0.91.

  4. 4.

    COGS stands for costs of goods sold. Our findings are robust to alternative profit metric specifications, including versions based on income (COMPUSTAT items NI or IB), earnings (COMPUSTAT items EBIT or EBITDA), and operating income (COMPUSTAT item OIBDP).

  5. 5.

    In addition to the multiple firm and industry controls included, we also controlled for competitors’ marketing, R&D, and operations capabilities directly, in alternative model specifications. Substantively, the findings remained unchanged. For the purpose of parsimony, we opted to not include these additional estimates with the findings reported.

  6. 6.

    The estimates we report are also consistent with those observed in less robust “levels-levels” models.

  7. 7.

    In Fig. 1, dimmed and dashed lines represent non-significant slopes/effects.

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Acknowledgments

The authors thank participants in the 2014 Academy of Marketing Science Annual Conference and participants in the 2015 AMA Winter Marketing Educators’ Conference for constructive comments.

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Correspondence to Hui Feng.

Additional information

Satish Jayachandaran served as Area Editor for this article.

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).

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Feng, H., Morgan, N.A. & Rego, L.L. Firm capabilities and growth: the moderating role of market conditions. J. of the Acad. Mark. Sci. 45, 76–92 (2017). https://doi.org/10.1007/s11747-016-0472-y

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Keywords

  • Marketing capabilities
  • Research-and-development capabilities
  • Operations capabilities
  • Firm growth
  • Munificence
  • Competitive dynamism