There's a direct correlation between the two events, I'd wager. She's being rewarded for taking decisive action and also being given an incentive to stay and see it through, where "it" is fixing IBM.
EDIT: yes, I used the wrong pronoun originally. The reaction... wow. No disrespect was intended, and I'd think that leeway would be given here on HN. Oh, well.
Concur. Hold onto her while she does the dirty work, but her days are numbered once Chrome is done. She's the hatchet (wo)man - and nobody will want her around post-cull. They'll want fresh untainted vision, buy-in from the survivors. She might as well give herself a post-dated 2016 3-score.
That sounded familiar and a quick search turned up a paper about it:
“The current study relies on a unique dataset
of all CEO transitions in Fortune 500 companies
over a 15-year period. We find that occupational
minorities are more likely than white men to
be promoted CEO of weakly performing firms;
and when firm performance declines during their
tenure, occupational minority CEOs are likely to
be replaced by white men, a phenomenon we term
the ‘savior effect.’”
Unfortunately the statistical analysis supporting that point sounds incorrect to me.
Their evidence consists of:
1. A claim that the mean return on equity of the previous CEO of -0.68 in the "savior" case (white CEO following a minority CEO) is significantly different from a value of 0.11 in the control case (white CEO following a white CEO). This can't be true because the low number of samples implies standard errors of 0.66 and 0.05 for the means. I'm computing a z-score of 1.18, corresponding to a two-sided p-value of 0.24, far above the claimed p<0.01.
2. A claim that the mean return on equity of the previous CEO is correlated with a binary variable describing whether we are in the savior case. While their measured correlation coefficient of -0.13 would indeed be statistically significant if it were measuring a correlation of normally distributed data, the use of a binary variable describing unbalanced classes means that 95% of the variance is concentrated on just 5% of the data (28 samples). Bootstrapping based on the published mean and deviation of each class shows a 0.11 standard deviation of the correlation coefficient, and more importantly a p-value of 0.12 which is once again non-significant.
An interesting feature is that the standard deviation of the return on equity differs a lot between classes. I assume that this is because the returns on equity are far from being normally distributed, and indeed data from another class with just 4 samples shows an abnormally low standard deviation, letting us reject the normal distribution hypothesis with p<0.001. Most returns are very close to zero, so necessarily some are much greater than the standard deviation. A few or even just one large-magnitude return in the 28 "savior" samples would suffice to create a spurious correlation.
I would contact the authors about it, but I would like someone here to confirm if my analysis makes any sense.
Which is exactly why she got the raise/bonus. It was probably agreed too before she ever took the job.
She'll do the dirty work, get paid VERY WELL for it, so the firm itself can save face. She'll leave next year with a few mill in her pocket. AND IBM will blame their woes on "her" plan.
EDIT: yes, I used the wrong pronoun originally. The reaction... wow. No disrespect was intended, and I'd think that leeway would be given here on HN. Oh, well.