Firm capabilities and growth: the moderating role of market conditions


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


  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.


  1. Aiken, L. S., & West, S. G. (1991). Multiple regression: Testing and interpreting interactions. London: Sage.

    Google Scholar 

  2. Aldrich, H. (1979). Organizations and environments. NJ: Prentice Hall.

    Google Scholar 

  3. Angulo-Ruiz, F., Donthu, N., Prior, D., & Rialp, J. (2014). The financial contribution of customer-oriented marketing capability. Journal of the Academy of Marketing Science, 42(4), 380–399.

    Article  Google Scholar 

  4. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297.

    Article  Google Scholar 

  5. Arellano, M., & Bover, O. (1995). Another look at the instrumental variable estimation of error-components models. Journal of Econometrics, 68(1), 29–51.

    Article  Google Scholar 

  6. Bahadir, S. C., Bharadwaj, S. G., & Srivastava, R. K. (2008). Financial value of brands in mergers and acquisitions: is value in the eye of the beholder? Journal of Marketing, 72(6), 49–64.

    Article  Google Scholar 

  7. Baltagi, B. (2001). Econometric analysis of panel data. Chichester: John Wiley & Sons.

    Google Scholar 

  8. Blundell, R., & Bond, S. (1998). Initial conditions and moment restrictions in dynamic panel data models. Journal of Econometrics, 87(1), 115–143.

    Article  Google Scholar 

  9. Danneels, E. (2011). Trying to become a different type of company: dynamic capability at Smith Corona. Strategic Management Journal, 32(1), 1–31.

    Article  Google Scholar 

  10. Day, G. S. (1994). The capabilities of market-driven organizations. Journal of Marketing, 58(4), 37–52.

    Article  Google Scholar 

  11. Day, G. S., Reibstein, D., & Shankar, V. (2009). Measuring innovation. Wharton School of Business Working Paper. Philadelphia: University of Pennsylvania.

  12. Dess, G. G., & Beard, D. W. (1984). Dimensions of organizational task environments. Administrative Science Quarterly, 29(1), 52–73.

    Article  Google Scholar 

  13. Dixit, A. K., & Pindyck, R. S. (1994). Investment under uncertainty. Princeton: Princeton University Press.

    Google Scholar 

  14. Dutta, S., Narasimhan, O., & Rajiv, S. (1999). Success in high-technology markets: is marketing capability critical? Marketing Science, 18(4), 547–568.

    Article  Google Scholar 

  15. Dutta, S., Narasimhan, O. M., & Rajiv, S. (2005). Conceptualizing and measuring capabilities: methodology and empirical application. Strategic Management Journal, 26(3), 277–285.

    Article  Google Scholar 

  16. Eisenhardt, K. M., & Martin, J. A. (2000). Dynamic capabilities: what are they? Strategic Management Journal, 21(1), 1105–1121.

    Article  Google Scholar 

  17. Grewal, R., & Slotegraaf, R. J. (2007). Embeddedness of organizational capabilities. Decision Sciences, 38(3), 451–488.

    Article  Google Scholar 

  18. Grossack, I. M. (1965). Towards an integration of static and dynamic measures of industry concentration. The Review of Economics and Statistics, 47(3), 301–308.

    Article  Google Scholar 

  19. Helfat, C. E., Finkelstein, S., Mitchell, W., Peteraf, M., Singh, H., Teece, D., & Winter, S. G. (2007). Dynamic capabilities: Understanding strategic change in organizations. Malden: Wiley-Blackwell.

    Google Scholar 

  20. Katsikeas, C. S., Morgan, N. A., Leonidou, L. C., & Hult, G. T. M. (2016). Assessing performance outcomes in marketing. Journal of Marketing, 80(2), forthcoming.

  21. Keats, B. W., & Hitt, M. A. (1988). A causal model of linkages among environmental dimensions, macro organizational characteristics, and performance. Academy of Management Journal, 31(3), 570–598.

    Article  Google Scholar 

  22. King, D. R., Slotegraaf, R. J., & Kesner, I. (2008). Performance implications of firm resource interactions in the acquisition of R&D-intensive firms. Organization Science, 19(2), 327–340.

    Article  Google Scholar 

  23. Kozlenkova, I. V., Samaha, S. A., & Palmatier, R. W. (2014). Resource-based theory in marketing. Journal of the Academy of Marketing Science, 42(1), 1–21.

    Article  Google Scholar 

  24. Krasnikov, A., & Jayachandran, S. (2008). The relative impact of marketing, research-and-development, and operations capabilities on firm performance. Journal of Marketing, 72(4), 1–11.

    Article  Google Scholar 

  25. Krush, M. T., Sohi, R. S., & Saini, A. (2015). Dispersion of marketing capabilities: impact on marketing’s influence and business unit outcomes. Journal of the Academy of Marketing Science, 43(1), 32–51.

    Article  Google Scholar 

  26. Kumbhakar, S. C., Wang, H., & Horncastle, A. P. (2015). A practitioner’s guide to stochastic frontier analysis using Stata. New York: Cambridge University Press.

    Book  Google Scholar 

  27. Lehmann, D. R., & Winer, R. S. (2009). Editorial: introduction to special issue on marketing and organic growth. International Journal of Research in Marketing, 26(4), 261–262.

    Article  Google Scholar 

  28. Levinthal, D. (2000). Organizational capabilities in complex worlds. In G. Dosi, R. Nelson, & S. Winter (Eds.), The nature and dynamics of organizational capabilities (pp. 363–379). Oxford: Oxford University Press.

    Google Scholar 

  29. Luo, X., & Donthu, N. (2006). Marketing’s credibility: a longitudinal investigation of marketing communication productivity and shareholder value. Journal of Marketing, 70(4), 70–91.

    Article  Google Scholar 

  30. Meyer, A. D., Tsui, A. S., & Hinings, C. R. (1993). Configurational approaches to organizational analysis. Academy of Management Journal, 36(6), 1175–1195.

    Article  Google Scholar 

  31. Mizik, N., & Jacobson, R. (2003). Trading off between value creation and value appropriation: the financial implications of shifts in strategic emphasis. Journal of Marketing, 67(1), 63–76.

    Article  Google Scholar 

  32. Mizik, N., & Jacobson, R. (2004). Are physicians “easy marks”? Quantifying the effects of detailing and sampling on new prescriptions. Management Science, 50(12), 1704–1715.

    Article  Google Scholar 

  33. Moorman, C., & Slotegraaf, R. J. (1999). The contingency value of complementary capabilities in product development. Journal of Marketing Research, 36(2), 239–257.

    Article  Google Scholar 

  34. Morgan, N. A. (2012). Marketing and business performance. Journal of the Academy of Marketing Science, 40(1), 102–119.

    Article  Google Scholar 

  35. Morgan, N. A., Slotegraaf, R. J., & Vorhies, D. W. (2009). Linking marketing capabilities with profit growth. International Journal of Research in Marketing, 26(4), 284–293.

    Article  Google Scholar 

  36. Morgan, N. A., Katsikeas, C. S., & Vorhies, D. W. (2012). Export marketing strategy implementation, export marketing capabilities, and export venture performance. Journal of the Academy of Marketing Science, 40(2), 271–289.

    Article  Google Scholar 

  37. Narasimhan, O., Rajiv, S., & Dutta, S. (2006). Absorptive capacity in high-technology markets: the competitive advantage of the haves. Marketing Science, 25(5), 510–524.

    Article  Google Scholar 

  38. Newbert, S. L. (2007). Empirical research on the resource‐based view of the firm: an assessment and suggestions for future research. Strategic Management Journal, 28(2), 121–146.

    Article  Google Scholar 

  39. Penrose, E. T. (1959). The theory of the growth of the firm. Oxford: Oxford University Press.

    Google Scholar 

  40. Piercy, N. (2007). Framing the problematic relationship between the marketing and operations functions. Journal of Strategic Marketing, 15(2–3), 185–207.

    Article  Google Scholar 

  41. Porter, M. E. (1980). Competitive strategy. New York: The Free Press.

    Google Scholar 

  42. Porter, M. E. (1985). The competitive advantage: Creating and sustaining superior performance. New York: The Free Press.

    Google Scholar 

  43. Ramaswami, S. N., Srivastava, R. K., & Bhargava, M. (2009). Market-based capabilities and financial performance of firms: insights into marketing’s contribution to firm value. Journal of the Academy of Marketing Science, 37(2), 97–116.

    Article  Google Scholar 

  44. Roodman, D. (2009). How to do xtabond2: an introduction to difference and system GMM in Stata. Stata Journal, 9(1), 86–136.

    Google Scholar 

  45. Ruekert, R. W., Walker, O. C., Jr., & Roering, K. J. (1985). The organization of marketing activities: a contingency theory of structure and performance. Journal of Marketing, 49(1), 13–25.

    Article  Google Scholar 

  46. Song, M., Droge, C., Hanvanich, S., & Calantone, R. (2005). Marketing and technology resource complementarity: an analysis of their interaction effect in two environmental contexts. Strategic Management Journal, 26(3), 259–276.

    Article  Google Scholar 

  47. Taylor, A., & Helfat, C. E. (2009). Organizational linkages for surviving technological change: complementary assets, middle management, and ambidexterity. Organization Science, 20(4), 718–739.

    Article  Google Scholar 

  48. Teece, D. J., Pisano, G., & Shuen, A. (1997). Dynamic capabilities and strategic management. Strategic Management Journal, 18(7), 509–533.

    Article  Google Scholar 

  49. Tirole, J. (1988). The theory of industrial organization. Cambridge: MIT Press.

    Google Scholar 

  50. Tuli, K. R., Bharadwaj, S. G., & Kohli, A. K. (2010). Ties that bind: the impact of multiple types of ties with a customer on sales growth and sales volatility. Journal of Marketing Research, 47(1), 36–50.

    Article  Google Scholar 

  51. Wooldridge, J. (2006). Introductory econometrics. Mason: Thompson Higher Education.

    Google Scholar 

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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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Corresponding author

Correspondence to Hui Feng.

Additional information

Satish Jayachandaran served as Area Editor for this article.


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).

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