Procrastination is the volitional delay of an intended task, despite believing that delay will be harmful. While not all delay is attributable to procrastination, procrastination is fundamentally characterised by delay. As much as 90% of the population have experience with procrastination, with around 20% in the general population and 50% of university students reporting problematic levels of chronic procrastination. Compared to their non-procrastinating peers, chronic procrastinators report lower levels of wellbeing, higher rates of depression, higher rates of alcohol and other drug use for coping, and poor health adjustment. Procrastinators tend to have lower salaries, shorter durations of employment, and a greater likelihood of being unemployed or underemployed. There is also a direct economic impact on the workforce, with office workers found to spend an average of 1.5 hours per work day procrastinating.
Despite its prevalence, the variability of tasks, time available, subjectivity, and individual differences render procrastination difficult to observe as it happens. Consequently, while correlates, antecedents, effects, and types of procrastination have been widely investigated, progress in this field is limited by several factors. In particular, few studies have accurately quantified delay associated with procrastination over time. As a consequence, there is limited evidence supporting the ability of trait measures of procrastination to predict delay, and few interventions aimed at reducing procrastination have been clearly associated with reduced delay. Recent developments in smartphone technology and Experience Sampling Method (ESM) applications have enabled intensive longitudinal observations of such dynamic phenomena with relative ease; however, such methodology and statistical modelling of delay have yet to be reliably applied to the study of procrastination.
To address the challenge of observing delay associated with procrastination, I conducted three studies of students enrolled in a 1st year psychology course: a small pilot study (N = 24) and two larger scale replications (Ns = 80 and 107) focusing on intensive longitudinal measurement of delay, procrastination scale validation, and an intervention to reduce procrastination respectively. Participant ages ranged from 17.38 to 65.85 years (M = 23.85, SD = 9.49) and 75% identified as female. Each study included a baseline survey of demographic and trait procrastination and personality variables, an ESM phase comprised of 28 SMS surveys over 14 days in the lead-up to submission of an assignment worth 30% of the course grade, and the collection of assignment submission date and mark from the course convenor. Participants in the ESM phase were randomly allocated into either an intervention or control condition, with participants in both conditions reporting their assignment progress, completion intent, and affect regarding their assignment progress. Participants in the intervention, but not the control, condition were messaged at the end of each ESM survey with open reflection prompts designed to reduce procrastination. Studies 1 and 3 also included follow up interviews with a small subsample of participants (N = 8) to garner first-hand perspectives of participation in the ESM component of the studies.
Through the application of multilevel model analyses, the presence of quantified delay curves in all three studies provides firm evidence that regular self-reporting of task progress using ESM is a robust and reliable method for measuring behavioural delay. The use of multilevel modelling in quantifying delay enabled the inclusion of mixed effects, where the predictive ability of several procrastination scales could be assessed. A trait measure of passive procrastination was found to reliably predict behavioural delay, whereas no association was found between a measure of active procrastination, a type of procrastination purported to be adaptive and deliberate, and delay. The intervention prompting regular reflection on factors thought to be related to procrastination that was embedded into the ESM phase of each study was found to significantly reduce delay in Studies 1 and 3, but not in Study 2. Between-study differences in this intervention effect were likely related to contextual differences as participants in Study 2 were aware that the research pertained to procrastination whereas those in the other studies were not informed of the focus on procrastination. In the follow-up interviews, participants reported that regularly reporting task progress, as well as the intervention reflection prompts, may have assisted with the reduction of procrastination. Analyses conducted into the relationships between trait procrastination, neuroticism, and state affect and delay revealed that neuroticism (emotional stability) moderated the relationship between trait procrastination and affect, and affect mediated the relationship between trait procrastination and task delay. Moreover, cross-lagged panel model analyses of inter-temporal changes in affect and delay showed that participants who reported greater task progress at an earlier time were likely to report higher positive affect at a subsequent time, whereas those reporting higher positive affect at an earlier time tended to report lower progress at a subsequent time.
Overall, the research offers three specific unique contributions to the body of knowledge. First, the use of ESM surveys of task progress is demonstrated to be a reliable method for measuring behavioural delay associated with procrastination. This is evidenced by the presence of accelerating delay curves, where assignment progress increases in a hyperbolic trajectory prior to a submission date. The reliable observation and modelling of delay is an oft-cited limitation of the field; thus, the replicated validation of this as a reliable method constitutes a valuable contribution. Second, multilevel mixed effects modelling is used to assess the ability of scales measuring different aspects of trait procrastination to predict behavioural delay, indicating that some trait procrastination measures are more predictive of behaviour than are others. The statistical method employed, and the use of task progress rather than study duration as the outcome, enabled the construct validity of the contentious ‘active’ form of procrastination to be challenged. This approach is proposed also to be a suitable method for assessing the behavioural efficacy of targeted interventions for reducing procrastination. Third, sending regular reflection prompts to randomly selected ESM recipients resulted in a significant reduction in behavioural delay in two of the three studies. This use of low-intensity reflection prompts delivered at a high frequency demonstrates smartphone use can be an effective medium for reducing procrastination without the need for intensive approaches requiring considerable commitment from both practitioners and participants. This intervention design sets an example for reducing delay in academia, with the method likely capable of being extended, with adaptation, to procrastination in other areas such as health behaviour change, personal finance, and collective action.