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Fuqua Faculty Conversations

Shane Dikolli

Shane Dikolli on The Effect of CEO Traits on Firm Policies and Outcomes

January 1, 2012

Shane Dikolli

Have you ever wondered whether CEO characteristics affect firm policies, strategies, compensation, reporting, and/or firm outcomes? What are the “CEO attributes” that researchers are measuring? Does CEO integrity have economic consequences?

View Professor Shane Dikolli’s “pre-reading” video and a recording of his February 28 live session to learn more about how CEO traits can have resounding effects within an organization.

Professor Dikolli’s session took place in February 2012.

View Professor Dikolli’s Bio (PDF)



Pre-recorded Video


Live Session Recording


  1. Given the election this year, I’m wondering if causation words research can/has been extended to other audiences like politicians?

    • Andrea,
      The short answer is no. In terms of whether it can be, there is no doubt that the transcripts of political debates could be subjected to the causation count software and a causation score could be determined, per candidate, per debate. I think if the rankings between candidates held over a series of debates it might provide a crude approximation of the relative integrity of the candidates. However, to be more persuasive for academic research purposes, the causation scores would need to correlate in a predictable way with an outcome that matters. One possibility is candidate voting patterns. If the causation score was positively correlated with inconsistency in voting patterns, it would help provide at least some persuasive evidence that the causation score is getting at the idea of integrity in a political candidate setting.

  2. Professor Dikolli,

    Great webinar! Very well organized and easy to follow. I am looking forward to the class on 2/28. Can you please provide a bit more detail on E-Loading or suggest some reading material to help understand it better?

    Thank you.

    Fayaz Qureshi ’07

    • Dear Fayaz,

      For further information on E-loading, you can read the original research study in which the measure was developed. The paper is entitled, “A Returns-Based Representation of Earnings Quality”, which was published in The Accounting Review in July 2006 (pp.749-780). The study was co-authored by five Fuqua professors at the time, including Frank Ecker, Jennifer Francis, Per Olsson, & Katherine Schipper who are all still on faculty at Fuqua. Due to copyright laws, I cannot post a final copy of the published article here. However, I can share a link to a publicly available document that is very close to the final published copy: http://bit.ly/Apkga1. Also, in case you have appropriate Ford Library password privileges, here’s a direct link to the published article in Fuqua’s Ford Library: http://bit.ly/zOFNjR

      Shane Dikolli

  3. Professor Dikolli: For many years I have been a consultant for and have been employing Predictive Index (PI) as part of my consulting activities. PI is a managerial assessment tool that provides very stark insight into natural workplace behaviors and motivations of individuals. We know that when looking at a CEO’s PI (particularly in context with his or her organization) we can make some very accurate predictions as far as behavior in that organization. When we take into account the corporate culture and the PI’s of other individuals within the organization we are able to help guide the CEO in implementation of strategy that lead to outcomes. It is an incredibly powerful tool, takes only a few minutes for an assessment but does require a skilled consultant to fully interpret. Gaining those skills are fairly easy which is one reason why PI has been used for over 50 years throughout the world. It certainly changed my view of workplace assessment. I have never seen a more accurate, less intrusive means of gaining workplace intelligence.
    David Isaacs ’78

    • Many thanks, David. It sounds like the PI is a proprietary tool that generates evidence consistent with some of the evidence that is starting to come out of the academic literature. Primarily, academics have access to public data only, which limits their investigations. But I think the fact that some of these investigations are starting to generalize insights across large samples suggests we might see a surge in attention and credibility applied to the proprietary tools. Additionally, the more that academics can access anonymous versions of the data from proprietary tools, the deeper the insights and the less limited the academic investigations will be.

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