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This technical manual outlines the science behind the Core Drivers Diagnostic, a fast, modern personality assessment built on the Five Factor Model. It explains how the tool was developed, validated, and tested for reliability, fairness, and predictive value, and how it can be used to support evidence-based talent decisions at scale.

This technical manual explains the science behind the Core Values Diagnostic, which measures what motivates people at work and why they engage. Built on Self-Determination Theory, it outlines how the assessment was developed, validated, and tested for fairness, and how its insights can be used to improve engagement, motivation, and retention.

This technical manual describes the science behind the Core Reasoning Assessment Suite, a set of adaptive cognitive ability tests measuring logical, numerical, and verbal reasoning. It outlines how the assessments were designed, validated, and tested for fairness, reliability, and predictive value, and how they support evidence-based hiring, development, and succession decisions.

This white paper explores why soft skills are essential in an AI-driven workplace and why most companies still struggle to measure and develop them effectively. Backed by science and real-world case studies, it offers a clear, evidence-based path forward for HR leaders and I-O psychologists looking to build agile, high-performing teams.

The future of work is arriving faster than ever, and with it, a new talent landscape. This white paper reveals five critical trends shaping the decade ahead, from the rise of AI-augmented roles to the growing importance of soft skills and continuous upskilling. Drawing on global research and case studies, it shows why human potential remains the most durable competitive edge and how HR leaders can prepare their people to thrive in an AI age.

This playbook explains how HR managers and leaders can run data-driven talent reviews that support promotion, succession planning, and leadership development decisions. Using behavioral science and evidence-based assessment principles, it shows how to move beyond opinion, performance ratings, and subjective labels by focusing on the behavioral capabilities that best predict future performance, adaptability, and growth.

This research paper examines how far AI can go in defining job fit by comparing large language models with subject-matter experts on soft skills. Drawing on data from more than 1,000 roles, it shows where AI aligns with expert judgment, where it falls short, and why human expertise remains essential for defining role-specific behavioral requirements. The paper also introduces a defensible, evidence-based approach to building fair soft-skill job profiles that organizations can actually use in hiring decisions.

This study examines how well broad versus more specific personality traits predict entrepreneurial success. It shows that while the Big Five are useful, narrower entrepreneurial traits are more consistent and accurate predictors across different types of entrepreneurial activity, offering clearer guidance for identifying and developing entrepreneurial potential.

This article looks at three emerging tools that are changing how organizations assess and develop talent: AI models that analyze digital footprints, technology that reads verbal and nonverbal behavior, and gamified assessments designed to improve the candidate experience. It explains how each approach works in practice, where they add real value, and where their limits still are.

This study explores how an entrepreneurial workplace culture shapes employee innovation, especially in times of economic pressure. Using data from over 400 employees, it shows that people innovate more when they feel engaged at work, and that an entrepreneurial culture helps individuals with entrepreneurial traits turn ideas into real output. The findings offer practical guidance for organizations that want to foster innovation from within.

This research explores whether people’s online language can be used to infer harder-to-measure personality risks, using Facebook status updates as the data source. It shows that certain dysfunctional personality tendencies are reflected more clearly in language than others, suggesting that digital behavior could offer a faster, lower-cost complement to traditional personality assessments.

This study explains how psychological differences drive performance and why assessments remain central to winning the war for talent. It shows how AI, big data, and biometrics are reshaping talent practices, and why organizations need evidence-based assessment, stronger data and AI literacy to use these tools well.

This paper examines how large language models respond to personality tests when prompted to take on specific roles. It shows that LLMs can adjust their answers to appear socially desirable and role-appropriate, raising important questions about how personality assessments should adapt in an era of widespread AI use.

This paper explores how specific emotional intelligence traits influence employee engagement at work. It shows that looking at individual facets, rather than a single overall score, explains engagement much more accurately, highlighting which traits act as personal resources that keep people motivated and involved.

This study examines how so-called “dark side” personality traits can either protect against burnout or increase vulnerability to it. Using data from emergency service workers, it shows that some traits are linked to higher resilience, while others raise burnout risk, and that resilience plays a key role in shaping this relationship.

This study looks at how personality risk traits and perceived organizational support shape employees’ intentions to quit. Using data from emergency service workers, it shows that certain dark-side traits increase turnover risk, especially when people feel unsupported at work, highlighting the protective role of organizational support.

This study examines what drives workplace communication and advice-seeking: emotional intelligence or cognitive ability. It finds that employees with higher cognitive ability are more likely to become go-to sources of knowledge, while emotional intelligence plays a much smaller role. People also tend to seek advice from colleagues who are similar to them, shaping how networks form at work.

This study explores how high-potential traits, dark personality traits, and engagement shape how successful people feel at work. It shows that engagement plays a key role in linking personality to perceived success, and that traits like curiosity, courage, and even narcissism are associated with higher engagement and self-rated success.

This paper reviews what really drives employability beyond economic conditions, focusing on the psychological factors that help people find and keep jobs. It highlights the gap between traditional research on career success and what employers actually value and proposes a clearer model to bridge that gap.

This article reviews how new technologies are reshaping talent identification, from digital profiling to workplace data analytics. It shows that while these tools are spreading quickly, solid scientific evidence hasn’t kept pace, and outlines where researchers and practitioners can play a bigger role in evaluating and improving modern assessment methods.

This book challenges the popular idea that “being yourself” is always the key to success. Drawing on decades of research, it shows how an overemphasis on authenticity can actually hold people and organizations back, and why adapting, learning, and listening to others often matters more.

This book challenges the popular idea that “being yourself” is always the key to success. Drawing on decades of research, it shows how an overemphasis on authenticity can actually hold people and organizations back, and why adapting, learning, and listening to others often matters more.

This book explores how AI is reshaping our lives, work, and relationships, for better and for worse. It argues that while AI can amplify human potential, it can also reinforce our worst habits, and that thriving in an AI-driven world will depend on strengthening human qualities like curiosity, empathy, and emotional intelligence.

This book examines why leadership roles so often go to overconfident and underqualified men, while more capable people, especially women, are overlooked. Drawing on decades of research, it shows how common leadership selection practices reward the wrong traits and explains how organizations can build better systems to identify and promote true leadership talent.

This book argues that organizations struggle with talent because they ignore what psychological science already knows about people at work. It shows how applying evidence-based approaches can improve hiring, development, engagement, and leadership decisions, replacing guesswork with data-driven talent management.