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Outsourcing and its implications for market success: negative curvilinearity, firm resources, and competition

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Abstract

Over the past few decades, outsourcing has become a widely used and researched means for firms to change their performance. In this article, we attempt to link outsourcing to the market success of firms, specifically their market share. We argue that although firms may be able to increase their market share through outsourcing, this is only true up to a point, beyond which market share actually decreases as a consequence of further outsourcing. There is, in other words, a negatively curvilinear (inverted U-shape) relationship between outsourcing and market share. We also hypothesize that the outsourcing–market share relationship is moderated negatively by both the strength of firm resources and the extent of competition in a firm’s market. We empirically confirm these arguments through a panel data analysis containing over 19,000 observations on manufacturing firms and offer some case examples to illustrate the mechanisms driving these results. Finally, we discuss implications for marketing research and practice.

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Notes

  1. Although we acknowledge this point, our actual measures of resource strength cannot be related to those of a firm’s suppliers. But through comparing them with competing firms, we are, ceteris paribus, comparing them with suppliers indirectly.

  2. To avoid any possible confusion, we are not making an argument here about whether or not outsourcing is more likely to involve highly labor-intensive activities. If an activity needed to satisfy customer demand is more labor intensive, and especially if it involves low-cost labor, this can be an indication that the underlying assets are easy to redeploy elsewhere (i.e., asset specificity is low), and a traditional transaction costs argument could be made. But, as noted above, our focus here is not on one activity but rather on all activities, and obtaining such measures across all activities for a large number of firms seems to be a next to impossible task.

  3. At the firm level, R&D investments could alternatively be seen as a means of accumulating technological resources, and the argument could again be that those resources drive a firm to lower its outsourcing level. The same argument could also be made for investments in marketing and sales, as per Hypothesis 5, which create brand-based resources inside a firm. Our data on R&D and marketing and sales, however, operate at the industry level and we therefore present the argument at this level.

  4. An argument could be made to look at a more specific set of firms, such as those in the assembly industry, as in Kotabe and Mol (2009). We replicated our main results, and they were the same for this subsample.

  5. We acknowledge that this variable is inherently related to our outsourcing variable. When firms outsource activities, it may reduce their number of employees while keeping their sales levels constant. It would therefore be preferable if another measure of productivity was available to us, but that is unfortunately not the case. We checked how strong the correlation between outsourcing and labor productivity was in the sample, through a direct correlation and by calculating variance inflation factors, and established it was not too high. We re-ran our main analysis excluding the labor productivity variable and this did not change the findings. Furthermore, we ran regressions where we tried to predict outsourcing and used labor productivity as an independent variable. Although labor productivity was a positive and highly significant predictor, it did not account for very much variance, and less variance than for instance the industry average level of outsourcing. Given that, we prefer to present the findings obtained here but acknowledge this as a limitation.

  6. We also re-ran the analysis including (time invariant) industry dummies and the findings are consistent. We do not include those dummies in the analyses, however, over concerns around multicollinearity—many of our variables are measured at the industry level.

  7. But, we alternatively apply OLS panel regression models for purposes of robustness. A Hausman test indicated that a fixed effects regression was preferable over random effects. Fixed effects panel models have several desirable properties. They help overcome the problem of omitted variables, since they allow for unobserved individual heterogeneity (by introducing a firm fixed effect, instead of time invariant variables such as industry dummies) that may be correlated with the regressors (Cameron and Trivedi 2005). This is helpful, especially in the absence of appropriate instrumental variables, because omitted variables are a key source of endogeneity problems. Our database does not contain any good instrumental variables, given that market share is correlated with just about every other firm or industry variable. In this fixed effects panel data analysis we are able to overcome heteroskedasticity and autocorrelation, through the use of the cluster estimation command, which produces robust standard errors. Autocorrelation, in particular, is a key problem in the data, given that a firm’s market share in one year is highly predictive of its market share during the next year. Our findings on curvilinearity were consistent with those presented below, which provides us with further confidence in the results.

  8. The analysis on this industry, which only makes up just over 10% of the sample, found support for the same hypotheses we find support for below.

  9. See Kotabe et al. (2007) for a detailed explanation on the qualitative data collection.

  10. The Brazilian association of automakers and the Brazilian association of auto suppliers.

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Acknowledgements

The authors would like to thank Statistics Netherlands (Centraal Bureau voor de Statistiek), in particular Robert Goedegebuure, for kindly allowing them to access the data employed in this study on CBS premises.

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Authors and Affiliations

  1. The Fox School of Business, Temple University, 1801 Liacouras Walk 559 Alter Hall (006-14), Philadelphia, PA, 19122-6038, USA

    Masaaki Kotabe

  2. Warwick Business School, University of Warwick, CV7 7AL, Coventry, UK

    Michael J. Mol

  3. Department of Marketing (SSB 458), University of Missouri-St. Louis, St. Louis, MO, 63121-4499, USA

    Janet Y. Murray

  4. School of Business, Department of Strategy, International Business and Entrepreneurship, Rutgers University, 227 Penn St. # 220, Camden, NJ, 08102, USA

    Ronaldo Parente

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  1. Masaaki Kotabe
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Correspondence to Masaaki Kotabe.

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Kotabe, M., Mol, M.J., Murray, J.Y. et al. Outsourcing and its implications for market success: negative curvilinearity, firm resources, and competition. J. of the Acad. Mark. Sci. 40, 329–346 (2012). https://doi.org/10.1007/s11747-011-0276-z

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