IDEAS home Printed from
   My bibliography  Save this paper

Industry segment effects and firm effects on firm performance in single industry firms


  • Houthoofd, Noël

    () (Hogeschool-Universiteit Brussel (HUB))

  • Hendrickx, Jef

    () (Hogeschool-Universiteit Brussel (HUB))


The purpose of the paper is to identify the sources of variation in firm performance. This is one of the cornerstones of strategy research, i.e. the relative importance of industry and firm level effects on firm performance. Multilevel analysis is well suited to analyze variance in performance when the data are hierarchically structured (industry segments consist of firms, firms operate within the context of industry segments). The Belgian industry studied is a service industry that consists of about 25 electrical wholesalers. Data were collected from 20 firms during the period 1998-2003 from responses to a questionnaire sent to all the firms in the market. The sample in the data set covers more than 95 percent of the market (in sales), as the missing firms were just fringe competitors. The results show that firm effects explain most of the variance in four performance variables. That bears out the importance of each firm having its own specific, idiosyncratic resources and competences. The explanatory power of firm effects varies by about 30 to 40 percent while the intra-industry effect explains around 10 percent of the variance. Even though firm effects are dominant, intra-industry effects explain a significant portion of the variance in firm level performance. The firm effect is smaller than in previous studies. The firm effect varies across the performance measures: firm effects are higher for returns on assets than for profit margins. The industry segment effect (or intra-industry effect) is more independent of the dependent variable. The industry segment effect is in line with previous studies on the strategic group effect. Top managers should carefully choose and monitor the intra-industry domain they are in.

Suggested Citation

  • Houthoofd, Noël & Hendrickx, Jef, 2012. "Industry segment effects and firm effects on firm performance in single industry firms," Working Papers 2012/17, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
  • Handle: RePEc:hub:wpecon:201217

    Download full text from publisher

    File URL:
    Download Restriction: no

    References listed on IDEAS

    1. Kellie L. Maske & Garey C. Durden & Patricia E. Gaynor, 2003. "Determinants of Scholarly Productivity among Male and Female Economists," Economic Inquiry, Western Economic Association International, vol. 41(4), pages 555-564, October.
    2. Laurens Cherchye & Bram De Rock & Frederic Vermeulen, 2008. "Analyzing Cost-Efficient Production Behavior Under Economies of Scope: A Nonparametric Methodology," Operations Research, INFORMS, vol. 56(1), pages 204-221, February.
    3. William Locke, 2005. "Integrating Research and Teaching Strategies: Implications for Institutional Management and Leadership in the United Kingdom," Higher Education Management and Policy, OECD Publishing, vol. 16(3), pages 101-120.
    4. Cinzia Daraio & Léopold Simar, 2005. "Introducing Environmental Variables in Nonparametric Frontier Models: a Probabilistic Approach," Journal of Productivity Analysis, Springer, vol. 24(1), pages 93-121, September.
    5. Baumol, William J, 1977. "On the Proper Cost Tests for Natural Monopoly in a Multiproduct Industry," American Economic Review, American Economic Association, vol. 67(5), pages 809-822, December.
    6. Fox, Kevin J & Milbourne, Ross, 1999. "What Determines Research Output of Academic Economists?," The Economic Record, The Economic Society of Australia, vol. 75(230), pages 256-267, September.
    7. Badin, Luiza & Daraio, Cinzia & Simar, Léopold, 2010. "Optimal bandwidth selection for conditional efficiency measures: A data-driven approach," European Journal of Operational Research, Elsevier, vol. 201(2), pages 633-640, March.
    8. Charnes, A. & Cooper, W. W. & Rhodes, E., 1978. "Measuring the efficiency of decision making units," European Journal of Operational Research, Elsevier, vol. 2(6), pages 429-444, November.
    9. repec:pal:jorsoc:v:59:y:2008:i:2:d:10.1057_palgrave.jors.2602445 is not listed on IDEAS
    10. Cinzia Daraio & Léopold Simar, 2007. "Conditional nonparametric frontier models for convex and nonconvex technologies: a unifying approach," Journal of Productivity Analysis, Springer, vol. 28(1), pages 13-32, October.
    11. De Witte, Kristof & Rogge, Nicky, 2011. "Accounting for exogenous influences in performance evaluations of teachers," Economics of Education Review, Elsevier, vol. 30(4), pages 641-653, August.
    12. Rogge, Nicky & De Witte, Kristof, 2009. "To publish or not to publish? On the aggregation and drivers of research performance," Working Papers 2009/42, Hogeschool-Universiteit Brussel, Faculteit Economie en Management.
    13. Laurens Cherchye & Willem Moesen & Nicky Rogge & Tom Puyenbroeck, 2007. "An Introduction to ‘Benefit of the Doubt’ Composite Indicators," Social Indicators Research: An International and Interdisciplinary Journal for Quality-of-Life Measurement, Springer, vol. 82(1), pages 111-145, May.
    14. L Cherchye & W Moesen & N Rogge & T Van Puyenbroeck & M Saisana & A Saltelli & R Liska & S Tarantola, 2008. "Creating composite indicators with DEA and robustness analysis: the case of the Technology Achievement Index," Journal of the Operational Research Society, Palgrave Macmillan;The OR Society, vol. 59(2), pages 239-251, February.
    15. Arocena, Pablo, 2008. "Cost and quality gains from diversification and vertical integration in the electricity industry: A DEA approach," Energy Economics, Elsevier, vol. 30(1), pages 39-58, January.
    16. Cazals, Catherine & Florens, Jean-Pierre & Simar, Leopold, 2002. "Nonparametric frontier estimation: a robust approach," Journal of Econometrics, Elsevier, vol. 106(1), pages 1-25, January.
    Full references (including those not matched with items on IDEAS)

    More about this item


    firm effect vs. industry effect; electrical wholesale sector; performance differences; multilevel analysis;

    NEP fields

    This paper has been announced in the following NEP Reports:


    Access and download statistics


    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:hub:wpecon:201217. See general information about how to correct material in RePEc.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Sabine Janssens). General contact details of provider: .

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    We have no references for this item. You can help adding them by using this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service hosted by the Research Division of the Federal Reserve Bank of St. Louis . RePEc uses bibliographic data supplied by the respective publishers.