Are We on the Same Wavelength? Four Steps for Moving From Talent Signals to Valid Talent Management Applications

Abstract

In their focal article, Chamorro-Premuzic, Winsborough, Sherman, and Hogan (2016) provide an overview of a number of new technologies with potentially significant implications for talent management related practices of industrial–organizational (I-O) psychology (both challenges and opportunities) that they label as “new talent signals.” These signals, they argue, are part of a revolution, due to increasing levels of social activity online as well as data-collection and mining techniques that have overtaken the conventional practice of talent identification in organizations. Their position is that these trends are leaving I-O psychologists in the dust in terms of our existing traditional theory, research, and methodologies. Their optimistic tone, however, also seems to suggest that while these approaches “have not yet demonstrated validity comparable with old school methods, they tend to disregard theory, and they pay little attention to the constructs being assessed (Chamorro-Premuzic et al., p. 634)” they may, in fact, have some legitimate basis in the identification of talent. Their point, of course, is that momentum in this area has surpassed human resources (HR), let alone I-O psychology, so that any concerns one might have about these trends are essentially “irrelevant.” This reminds us of a reference to lemmings going off a cliff together.

Putting an End to Bad Talent Management: A Call to Action for the Field of Industrial and Organizational Psychology

“…Perhaps most recently, we are seeing a trend toward those who believe that big data applications will finally solve this equation using complex algorithms based on web scraping and email mining similar to Chamorro-Premuzic et al’s (2016) talent signals concept. Although we have our concerns about making decisions with limited data in some of these areas (Church & Silzer, 2016), the bottom line is if we as I-O psychology professionals do not get there in time, others will.…”

ASAS-NANP Symposium: Mathematical Modeling in Animal Nutrition: The progression of data analytics and artificial intelligence in support of sustainable development in animal science

“…The SOS results in some attention deficit disorder at the organizational level because technologists cannot maintain a consistent direction of their perceived (desired) mission ( Church et al, 2017 ; Roberts, 2011 ). In some social sciences, the recommendation has been to stay away from these shiny new objects in practice ( Church and Silzer, 2016 ) until their practical efficiency and efficacy are proven. Thus, it seems prudent that the animal science community avoid the SOS by seeing beyond the frontiers of current knowledge to understand and control the mechanisms that govern (and limit) natural processes while improving productivity and efficiency to stewardship agriculture towards sustainable and responsible achievements.…”

Leadership in the time of COVID: Should we really throw the baby out with the bathwater?

“…Echoing the point made by the authors in the focal article, any new construct should add incremental value to our existing theories and predictive models. We may find that the "new" is a waste of time and resources or, at the more extreme end as noted by Church and Silzer (2016), an unwarranted risk to the organization.…”