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Can Effective Risk Management Signal Virtue-Based Leadership?

Journal of Business Ethics volume 129, pages 115–130 (2015)Cite this article

We do not act rightly because we have virtue or excellence, but we rather have those because we have acted rightly. We are what we repeatedly do. Excellence, then, is not an act but a habit.—Aristotle.

Abstract

Using exploratory factor analysis on a unique dataset of global executives, we find that their perceptions of their national government’s risk management effectiveness are largely driven by two latent factors: leadership virtue, and governance. We show that the leadership virtue signal is potentially a stronger signal (3.19 times more correlated with risk management perceptions than the governance indicator). We hypothesize that this may be because making decisions and taking actions to manage risk is a continuous process requiring inter alia foresight and moral discipline in looking to the interests of others and acting in service to those interests above self-interest. This suggests at least two propositions for further testing, for which, we offer rhetorical argument and anecdotal evidence at the end of this paper and suggest methodologies for further testing. To our knowledge, this is the first paper to uncover this connection empirically between national risk management and leadership virtue.

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Fig. 1

Notes

  1. In the U.S., this was further propelled by the Sarbanes–Oxley legislation in 2002.

  2. The principal–agent problem is the well-studied issue that arises when there is a separation between owners and managers (or employees). If the managers' incentives are not aligned with the owners, the principal’s cost of monitoring the agent can increase and thus reduce the profitability derived from the owner’s assets or investment.

  3. “The World Economic Forum is an independent international organization committed to improving the state of the world by engaging business, political, academic and other leaders of society to shape global, regional and industry agendas. Incorporated as a not-for-profit foundation in 1971, and headquartered in Geneva, Switzerland, the Forum is tied to no political, partisan or national interests” (www.weforum.org).

  4. For a detailed description of the data, survey, and methodology, see the 2012 report at http://reports.weforum.org/global-competitiveness-report-2012-2013 (retrieved 11 February 2013) (Schwab 2012).

  5. See Appendix 1 for a table of the countries by their median score on their national government’s risk management effectiveness.

  6. Four types of factor analyses were used to explore the data (Principle, Principle-Component, Iterated Principle Component, and Maximum Likelihood). All of them found similar structures. We report the results from the most often used Iterated Principle Component approach. All found 12–15 factors with eigenvalues above 1 (the Kaiser rule). Since this rule tends to over-extract factors when the number of variables is greater than 30, we took the low end choosing to rotate 12 factors. The number of factors is also supported theoretically by the design of the competitiveness index that identifies 12 pillars for competitiveness that the survey is meant to help measure. Analysis of the structure as well as regression analysis with the risk management question showed, however, only 10 factors to be relevant (Factors 11 and 12 had fewer than three variables loaded highly on them). For this reason, the analysis proceeds with rotating 10 factors.

  7. The question on risk management effectiveness was excluded for the exploratory factor analysis, so that it could be used as a separate variable.

  8. Factor rotation is done to make patterns more evident. The varimax procedure is the most typical orthogonal rotation. The varimax rotation maximizes the variance between factors; the rotated factor scores are the squared correlation of the variable with the latent factor, cf. Brown (2009).

  9. A loading of 0.6 means that 36 % of the variance of the variable is associated with that Factor.

  10. A table with all 148 variables and their rotated load scores is available upon request.

  11. See Rummel (1970) at http://www.hawaii.edu/powerkills/UFA.HTM#P5.2 (retrieved 20 March 2014).

  12. For a discussion of the indicators and the methodology, see (Kaufmann et al. 2009). Note that one of the sources is the EOS.

  13. http://www.businessmonitor.com/home/Risk/methodological.html (retrieved 8 February 2013).

  14. See the Methodology at www.prsgroup.com/ICRG_Methodology.aspx#PolRiskRating (retrieved 9 July 2013) for a detailed description of the rating.

  15. Mayunga (2007) defines social capital as the trust, norms, and networking that facilitate collective action and trade (coordination and cooperation for mutual benefit).

  16. www.prsgroup.com/ICRG_Methodology.aspx#PolRiskRating.

  17. The partial correlation estimates only the correlation between a country’s risk management perception and the given correlate while holding the other variables constant.

  18. Appendix shows the ANOVA tests for the stages of development and risk management question. The means of the risk management variable are significantly different between the different stages of development suggesting that this should be used as a control within stage-fixed effects that might be driven by the level of development.

  19. As we saw from the correlations with the Good Governance Indicators, the competence aspect is captured largely by Factors 1, 3, and 6.

  20. Some of the higher correlation is likely driven by the statistical fact that the Leadership Virtue factor is derived from the same sample as the risk management effectiveness question whereas the governance indicator is a composite of many different sources and samples.

  21. OECD “What Does ‘Resilience’ Mean for Donors,” (2013).

  22. Risk Culture is defined by Levy et al. (2010) as “the norms of behavior for individuals and groups within an organization that determine the collective ability to identify and understand, openly discuss and act on the organization’s future and current risks.”

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Acknowledgments

This paper has greatly benefited from comments from my colleagues at the Risk Center, Howard Kunreuther, Erwann Michel-Kerjan, Jeff Czajkowski, and Carol Heller. Mike Useem at Wharton’s Leadership Center also provided valuable feedback. I thank Lee Howell, Managing Director at the World Economic Forum, for discussions, insights, and useful tests for the data; and Florian Ramseger, my former colleague on the Risk Response Network, for his support and advice on this project. I am also indebted to the Competitiveness Team at the World Economic Forum for putting the risk question on their survey and providing me with the relevant data and helpful comments on drafts of the paper. This research benefited from the support of the Wharton Managing and Financing Extreme Events project and of the Z Zurich Foundation. Lastly, I would like to thank an anonymous reviewer for comments that strengthened the clarity and presentation of this paper. Of course, any remaining errors are all my own.

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Authors and Affiliations

  1. Center for Risk Management and Decision Processes, The Wharton School, University of Pennsylvania, 527.4 Jon H. Huntsman Hall, Philadelphia, PA, 19104, USA

    Karen A. Campbell

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  1. Karen A. Campbell
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Correspondence to Karen A. Campbell.

Appendices

Appendix 1: Country Median Score on Risk Management Effectiveness Question

The Median response to the Risk Management Effectiveness question for all countries was a four (4); however. the mode was three (3). Table 9 clusters countries by their median score.

Table 9 Country with median risk management effectiveness score—scale from 1 to 7, (higher numbers are better)
Full size table

Appendix 2: Analysis of Variance of Risk Management Effectiveness Perception

The five stages of development identified by the World Economic Forum Global Competitiveness Report are (1) Factor Driven; (2) Transition from Factor Driven to; (3) Efficiency Driven; (4) Transition from Efficiency Driven to; and (5) Innovation Driven. There are significant differences between all stages of development in the mean perception of their respective countries’ risk management effectiveness. Thus, this was used as a control in the regressions of the differences.

ANOVA test of Risk Management effectiveness questions by stage of development (Bonferroni tabulate)

Summary:

Dev_stage

Mean

SD

Freq.

1

3.1134979

1.5593684

3119

2

3.7836186

1.7158019

1636

3

3.5920432

1.5160048

2966

4

3.9868369

1.6053787

2507

5

4.432659

1.5907827

3460

Total

3.7906926

1.6550507

13688

Analysis of Variance

Source

SS

df

MS

F

Prob > F

Between groups

3069.86415

4

767.466037

305.08

0.0000

Within groups

34421.4701

13683

2.51563766

  

Total

37491.3342

13687

2.73919297

  

Bartlett’s test for equal variances: χ 2(4) = 35.6364 Prob > χ 2 = 0.000;

Comparison of Risk Management Effectiveness questions by dev_stage

(Bonferroni correction for significance)

Row Mean-Col Mean

1

2

3

4

2

0.670121

   
 

0.000

   

3

0.478545

−0.191575

  
 

0.000

0.001

  

4

0.873339

0.203218

0.394794

 
 

0.000

0.001

0.000

 

5

1.31916

0.64904

0.840616

0.445822

 

0.000

0.000

0.000

0.000

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Campbell, K.A. Can Effective Risk Management Signal Virtue-Based Leadership?. J Bus Ethics 129, 115–130 (2015). https://doi.org/10.1007/s10551-014-2129-4

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Keywords

  • Ethics
  • Governance
  • Leadership
  • Risk management