Stronger Together: Personality, Intelligence and the Assessment of Career Potential

Franziska Leutner; Tomas Chamorro-Premuzic

doi:10.3390/jintelligence6040049

Stronger Together: Personality, Intelligence and the Assessment of Career Potential

Journal of Intelligence ◽

10.3390/jintelligence6040049 ◽

2018 ◽

Vol 6 (4) ◽

pp. 49 ◽

Cited By ~ 2

Author(s):

Franziska Leutner ◽

Tomas Chamorro-Premuzic

Keyword(s):

Individual Differences ◽

Real World ◽

Predictive Power ◽

Organizational Psychology ◽

Data Driven ◽

Applied Psychology ◽

Organizational Contexts ◽

Industrial Organizational Psychology ◽

Intelligence Measures ◽

Data Driven Decisions

Personality and intelligence have a long history in applied psychology, with research dating back more than 100 years. In line, early developments in industrial-organizational psychology were largely founded on the predictive power of personality and intelligence measures vis-à-vis career-related outcomes. However, despite a wealth of evidence in support of their utility, the concepts, theories, and measures of personality and intelligence are still widely underutilized in organizations, even when these express a commitment to making data-driven decisions about employees and leaders. This paper discusses the value of personality and intelligence to understand individual differences in career potential, and how to increase the adoption of theories and tools for evaluating personality and intelligence in real-world organizational contexts. Although personality and intelligence are distinct constructs, the assessment of career potential is incomplete without both.

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Against generalisation: Data-driven decisions need context to be human-compatible

Business Information Review ◽

10.1177/02663821211061986 ◽

2021 ◽

pp. 026638212110619

Author(s):

Sharon Richardson

Keyword(s):

Artificial Intelligence ◽

Real World ◽

Data Science ◽

Machine Learning Algorithms ◽

Data Driven ◽

Theoretical Frameworks ◽

The Past ◽

Real World Applications ◽

Contextual Framework ◽

Data Driven Decisions

During the past two decades, there have been a number of breakthroughs in the fields of data science and artificial intelligence, made possible by advanced machine learning algorithms trained through access to massive volumes of data. However, their adoption and use in real-world applications remains a challenge. This paper posits that a key limitation in making AI applicable has been a failure to modernise the theoretical frameworks needed to evaluate and adopt outcomes. Such a need was anticipated with the arrival of the digital computer in the 1950s but has remained unrealised. This paper reviews how the field of data science emerged and led to rapid breakthroughs in algorithms underpinning research into artificial intelligence. It then discusses the contextual framework now needed to advance the use of AI in real-world decisions that impact human lives and livelihoods.

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Zedeck to Expand Journal of Applied Psychology: Journal's focus to Include Aspects of Applied Psychology Outside of Industrial/ Organizational Psychology

PsycEXTRA Dataset ◽

10.1037/e300012003-039 ◽

2002 ◽

Author(s):

Deborah Smith

Keyword(s):

Organizational Psychology ◽

Applied Psychology ◽

Industrial Organizational Psychology

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Uncovering mental structure through data-driven ontology discovery

10.31234/osf.io/fvqej ◽

2018 ◽

Cited By ~ 3

Author(s):

Ian W Eisenberg ◽

Patrick Bissett ◽

Ayse Zeynep Enkavi ◽

Jamie Li ◽

David MacKinnon ◽

...

Keyword(s):

Cognitive Processes ◽

Real World ◽

Predictive Power ◽

Empirical Relationship ◽

Self Regulation ◽

Self Report ◽

Data Driven ◽

Psychological Science ◽

Individual Traits ◽

Cumulative Knowledge

Psychological sciences have identified a wealth of cognitive processes and behavioral phenomena, yet struggle to produce cumulative knowledge. Progress is hamstrung by siloed scientific traditions and a focus on explanation over prediction, two issues we address by examining individual differences across an unprecedented range of behavioral tasks, self-report surveys, and real-world outcomes. We derive a cognitive ontology and evaluate the predictive power of many psychological measurements related to self-regulation. Though both tasks and surveys putatively measure self-regulation, they show little empirical relationship. Within tasks and surveys, however, the ontology reveals opportunities for theoretic synthesis and identifies stable individual traits. Additionally, surveys predict self-reported real-world outcomes while tasks largely do not. We conclude that data-driven ontologies lay the groundwork for a cumulative psychological science.

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