Higher-Order Cognitive Skills Drove Learning Agility More Than Basic Abilities
Learning agility, in this study, was not treated as a vague workplace virtue. It was measured as a multidimensional behavioral construct in Generation Z employees working in technology-driven settings where change, uncertainty, and rapid adaptation are routine rather than exceptional. The central question was whether the cognitive machinery behind agility comes mainly from basic abilities such as reasoning, memory, attention, coordination, and perception, or from higher-order functions such as creativity, critical thinking, and problem-solving.
The answer was clear. Higher-order cognitive abilities carried the stronger signal.
A total of 270 employees in Indonesia took part, split evenly between 135 workers in established companies and 135 in start-up companies. Basic cognitive abilities were assessed with CogniFit, while creativity was measured with the Divergent Association Task, critical thinking with the Watson-Glaser Critical Thinking Appraisal, and problem-solving with the FourSight framework. Learning agility was then modeled against those inputs using Partial Least Squares Structural Equation Modeling. The resulting pattern showed that creativity and problem-solving had consistent positive relationships with learning agility across organizational settings, while basic abilities were less decisive and, in some cases, dependent on context.
Creativity and Problem-Solving Showed the Most Consistent Link
The strongest and most stable associations emerged from creativity and problem-solving. In practical terms, employees who could generate novel associations, tolerate ambiguity, and move from one framing of a problem to another were better positioned to adapt when routines shifted or when familiar tools no longer fit the task. Problem-solving likely mattered for the same reason: adaptation in volatile work settings depends less on recalling fixed procedures than on selecting and recombining strategies under pressure.
This pattern fits the structure of fast-moving digital workplaces. Start-up environments often compress timelines, blur roles, and demand frequent course correction. Established companies may offer more defined processes, but even there, new technology and organizational restructuring can quickly expose the limits of narrow procedural competence. In both settings, cognitive flexibility appears to matter more than raw efficiency in simple processing tasks.
Basic cognitive abilities still had a role, but not the dominant one. Reasoning, critical thinking, and perception showed effects that shifted by organizational context, suggesting that these faculties do not translate into learning agility in a uniform way. A person may reason well, perceive patterns accurately, or think critically, yet still struggle to adapt if the environment does not reward rapid experimentation or if the work structure leaves little room for independent adjustment.
Why Generative Thinking May Matter More Than Cognitive Efficiency
The findings point toward a useful distinction in workplace psychology. Learning agility is not simply a measure of how quickly someone can process information. It is closer to a behavioral capacity for absorbing novelty, revising assumptions, and translating experience into future action. That process depends heavily on generative cognition — the ability to produce new possibilities — and evaluative cognition — the ability to judge which possibilities are worth pursuing.
Creativity supports the first step. It expands the number of solutions an employee can imagine when the standard answer fails. Problem-solving supports the second step. It helps the individual test those options against constraints, priorities, and shifting goals. Together, those functions map onto the kind of adaptation modern organizations often demand: not just learning new content, but reorganizing thought quickly enough to keep pace with change.
Basic cognition likely remains a foundation, yet the study suggests it is not sufficient on its own. Strong attention or memory may help with absorbing instructions, but learning agility in dynamic work settings requires more than retention. It requires synthesis, flexibility, and the willingness to revise mental models when the old one stops working. In environments where uncertainty is constant, those higher-level processes may become the real bottleneck or the real advantage.
Established Companies and Start-Ups Did Not Reward the Same Cognitive Mix
One of the more interesting aspects of the analysis was the context-specific behavior of certain abilities. Reasoning, critical thinking, and perception did not operate identically across established and start-up companies. This suggests that organizational structure shapes which cognitive skills are converted into agile behavior.
In a highly structured company, employees may have access to clearer role boundaries, more stable workflows, and more explicit guidance. Under those conditions, strong reasoning may reinforce performance by helping workers interpret procedures and choose between known options. In a start-up, however, the same skill set may be less visible if the work itself changes too quickly for linear problem-solving to dominate. Perception may also vary in usefulness depending on whether the employee is scanning a stable workflow or navigating a shifting product, team, or market environment.
The study’s design cannot prove causality, and it does not claim that one cognitive style is universally superior in every setting. What it does show is that the path from cognition to agility is not flat. Different workplace ecosystems appear to amplify different mental skills, and the most adaptable employees may be the ones who can move between them.
For employers, the practical implication is straightforward. Selection and development strategies that focus only on baseline cognitive efficiency may miss the abilities most relevant to modern adaptability. Training that strengthens creativity, flexible problem framing, and structured problem-solving may be more useful than relying on memory or speed alone. For employees, the same logic applies in reverse: adaptability at work may depend less on being the fastest thinker and more on being the most elastic one.
The study leaves several questions unresolved, including how these relationships would look in older employees, in different countries, or across industries outside technology-intensive work. It also does not determine whether cognitive abilities drive learning agility over time or whether agile workers simply become better at expressing those abilities in workplace settings. Still, the data are strong enough to narrow the discussion: in this cohort, learning agility rose most clearly where creativity and problem-solving were strongest, while basic cognitive skills played a more variable supporting role.