I thought this article was a straightforward look at the factors involved in utility. Isn't it a little unrealistic to assume we will have an accurate utility estimate? Meaning, even taking into account people who decline jobs, there are numerous reasons why an employee may not be performing to his/her full potential.
What are some other factors that may bias the utility estimate? What about the applicant pool or recruitment? How do they affect utility estimates?
Wouldn’t the size of the applicant pool be a moderator of this potential problem of overestimating utility? That is, wouldn’t this be more of a problem with a small sample size of applicants with varying levels of ability than with a large sample size? Additionally, wouldn’t it seem logical that at least some of the top applicants would remain interested in the job position through the remainder of the application process? In other words, while I realize that some top applicants will be lost to other jobs, isn’t it still pretty likely that the individuals hired for a position would still be relatively close in ability to these “lost” top applicants?
•Regarding their findings of utility gain estimates being overestimated from 30-80%. Aren’t conservative or meeting the minimum required productivity/utility (i.e., profit) best and safe for companies and those who present the predictions in forecasting or getting an estimate of selection utility models?
I concur with Shane too, in that there are other factors not controlled for that are effecting the utility measures thus maybe making the estimations too large and inaccurate.
So Murphy is simply saying (7 years after Schmidt et al.), don’t get that excited. Top scorers will often turn companies down as they have many options. The estimate of productivity gained (utility) needs to reflect this. What other boundary conditions of utility equations exist?
Do the findings of Murphy that utility calculations may substantially overestimate the economic utility of a selection procedure lend any credibility to the instincts of managers cited in the Winkler et al (2010) paper that the monetary estimates of utility analysis seem to large?
I think it is important to consider the influence that rejections have on utility, but I think that Murphy is overestimating it or is just unclear. I think other factors need to be taken into consideration such as the cost of testing, the amount of work that it takes in deciding who to select, and the time interval between start date and the job offer. Do you agree or disagree with this?
I thought this article was a straightforward look at the factors involved in utility. Isn't it a little unrealistic to assume we will have an accurate utility estimate? Meaning, even taking into account people who decline jobs, there are numerous reasons why an employee may not be performing to his/her full potential.
ReplyDeleteWhat are some other factors that may bias the utility estimate? What about the applicant pool or recruitment? How do they affect utility estimates?
Wouldn’t the size of the applicant pool be a moderator of this potential problem of overestimating utility? That is, wouldn’t this be more of a problem with a small sample size of applicants with varying levels of ability than with a large sample size? Additionally, wouldn’t it seem logical that at least some of the top applicants would remain interested in the job position through the remainder of the application process? In other words, while I realize that some top applicants will be lost to other jobs, isn’t it still pretty likely that the individuals hired for a position would still be relatively close in ability to these “lost” top applicants?
ReplyDelete•Regarding their findings of utility gain estimates being overestimated from 30-80%. Aren’t conservative or meeting the minimum required productivity/utility (i.e., profit) best and safe for companies and those who present the predictions in forecasting or getting an estimate of selection utility models?
ReplyDeleteI concur with Shane too, in that there are other factors not controlled for that are effecting the utility measures thus maybe making the estimations too large and inaccurate.
ReplyDeleteSo Murphy is simply saying (7 years after Schmidt et al.), don’t get that excited. Top scorers will often turn companies down as they have many options. The estimate of productivity gained (utility) needs to reflect this. What other boundary conditions of utility equations exist?
ReplyDeleteAlong with everyone talking about moderators, what are some organization level factors that might effect the utility analysis?
ReplyDeleteDo the findings of Murphy that utility calculations may substantially overestimate the economic utility of a selection procedure lend any credibility to the instincts of managers cited in the Winkler et al (2010) paper that the monetary estimates of utility analysis seem to large?
ReplyDeleteI think it is important to consider the influence that rejections have on utility, but I think that Murphy is overestimating it or is just unclear. I think other factors need to be taken into consideration such as the cost of testing, the amount of work that it takes in deciding who to select, and the time interval between start date and the job offer. Do you agree or disagree with this?
ReplyDelete