Job Analysis Overview

Principles

Definition

Job analysis is a systematic process for collecting and analyzing information about the tasks, duties, and responsibilities of a job, as well as the knowledge, skills, abilities, and other characteristics required for successful performance. This foundational methodological approach provides essential data for various human resource management functions and organizational design decisions.

Ontological type

HR Applications

Core Methods

Common Frameworks

Key developments

Key Figures

Standardized Measurement Foundations era

J. Richard Hackman [1], whose work in this era was associated with Yale University [3] and Western University [4], helped advance standardized job analysis through measurement-driven approaches. His key contributions include the Development of the Job Diagnostic Survey [7], a replicable instrument for measuring job content and motivation, and the Effects of Job Redesign: A Field Experiment [8], which demonstrated that redesigning tasks could influence performance and satisfaction, thereby advancing a standard framework for diagnostic measurement in this era. Greg R. Oldham [2], associated with the University of Illinois Urbana-Champaign [5] and Urbana University [6] during this era, contributed to the same measurement framework through the Development of the Job Diagnostic Survey [7], a collaboration with Hackman. These contributions were important for the era because they established replicable measurement tools and cross-job taxonomies that supported systematic diagnostic approaches to task design while addressing concerns of validity, fairness, and demographic bias.

Profile-Based Fit Paradigm era

Murray R. Barrick [1] is associated with the University of Iowa [3] and Michigan State University [4] in this era. His key contribution is the meta-analysis of the Big Five personality dimensions and job performance [5], which advanced trait-based fit in standardized selection systems and demonstrated incremental validity for predicting performance across roles. Michael K. Mount [2] is associated with the University of Iowa [3] and Michigan State University [4] in this era. His work on the Big Five personality dimensions and job performance meta-analysis [5] similarly highlighted the value of personality profiles for predicting job performance and reinforced the era's profile-based fit paradigm.

Data-Driven Design and AI era

Arnold B. Bakker [1], associated with Erasmus University Rotterdam [3] and University College London [4], has been a central figure shaping data-driven approaches to job design in this era. His key contributions include Development and validation of the job crafting scale [6] and Job crafting: Towards a new model of individual job redesign [7], which provided validated measures and a theoretical framework essential for measuring and applying job crafting in organizational design. Maria Tims [2], connected with Erasmus University Rotterdam [3] and University of Amsterdam [5], contributed to this era's understanding of job crafting. Her collaborative work on Development and validation of the job crafting scale [6] and Job crafting: Towards a new model of individual job redesign [7] helped establish reliable metrics and practical theories that support data-driven HR analytics and continuous job redesign.