Introduction

Motivation is a dynamic phenomenon that is generally poorly defined and measured. Measurement designs and techniques are predominantly addressing individual differences, which are typically assumed to be stable. Motivation is about changes in behaviour, however, which are not adequately captured by these measurement models.

Objectives

The objectives of this paper are to (1) to critically review prevailing differential measurement models that are applied to overcome challenges of dynamic measurement, and (2) to propose a new approach that considers the dynamic nature of motivation.

Methodology

Building on recent work, which has distinguished between differential and temporal approaches to measurement and prediction (see Roe, 2014), we discuss the merits and limitations of prevailing differential methods.
Next, we consider how researchers have attempted to overcome the challenge of dynamic measurement with the help of state-trait models, and discuss conceptual and logical problems, limiting the use of these models. To overcome these conceptual problems we propose a new measurement model, which focuses on individuals’ dynamic trajectories, defined with reference to a time frame of length L, starting at moment M, and comprising N observations. We show how this model can be used to describe subjects’ motivational states and to redefine traits in a dynamic way. We illustrate the logic and utility of this approach for work engagement – a well-investigated phenomenon in the current literature on work motivation – using a data set collected with 61 naval cadets who completed a daily diary over a period of 34 days.

Results and Conclusions

State-traits models are based on differential premises that limit their capability to grasp the dynamics of motivation. Our alternative state-trait model starts from a temporalist position and posits that states shall be measured within each individual subject and that individual differences in state trajectories shall be assessed afterwards.