Organizational commitment has long been a central construct in the I/O psychology, organizational behaviour, and management literatures due to its links to important outcomes, including retention, performance, and well-being. It has also been incorporated into other theories (e.g., leadership, justice, and HRM) as a proximal outcome and/or mediator of more distal outcomes. Over the last decade, interest and research on organizational commitment has expanded rapidly to countries outside of North America where interest originated.
The last comprehensive meta-analysis of commitment research was conducted over 20 years ago; subsequent analyses have been limited in scope due to the size of the literature. Consequently, more recent meta-analytic reviews tend to address isolated correlates and are scattered across disciplines, making integration of findings difficult.
Recent developments in artificial intelligence (AI) now make it possible to deal with the extensive and ever expanding commitment literature. Indeed, AI can screen studies for relevance and extract the data required for analysis with high reliability and accuracy. Once extracted into a common database, AI can aid in categorizing variables based on label and measure to isolate core constructs. Importantly, this database can be shared among researchers, and data from new studies can be added to update previous analyses and address new research questions.
In this presentation, we will provide an overview of this AI-assisted approach to conducting meta-analyses using organizational commitment as the focal variable. For example, we will illustrate how data extracted from studies conducted across countries/cultures can be combined with other country-level data to identify and explain differences in the nature and strength of commitment. We will also demonstrate how key antecedent and outcome variables can be included in the analyses, and how changes in commitment, and its correlations with other variables, can be tracked over time using more than 40 years of commitment research.