Research paper
Further development and testing of the metacognitive model of procrastination: Self-reported academic performance

https://doi.org/10.1016/j.jad.2018.07.018Get rights and content

Highlights

  • Poorer academic performance is associated with unintentional procrastination.

  • The metacognitive model of procrastination explains 13% of the variance in academic performance.

  • Interventions targeting metacognitive beliefs may help to optimize academic performance.

Abstract

Background: procrastination is highly prevalent amongst students and impairs academic performance. The metacognitive model of procrastination explains a significant proportion of unintentional procrastination variance. However, the model has yet to be tested using academic performance as the dependent variable. We tested whether the metacognitive model of procrastination explained self-reported academic performance (AP). Methods: a convenience sample of 204 current undergraduate and postgraduate students completed a battery of online questionnaires that measured intentional and unintentional procrastination, metacognitions about procrastination, AP, and depression. We conducted a series of correlation analyses and a path analysis (based on the metacognitive model of procrastination) that specified AP as the dependent variable. Results: the correlation analyses indicated that there are significant, negative associations between AP and depression, AP and negative metacognitions about procrastination, and AP and unintentional procrastination. The tested model was a good fit of the data and explained 13% of the variance in AP. Limitations: this study is cross-sectional. Conclusions: our findings provide further support for the metacognitive model of procrastination, indicating that novel interventions that target metacognitions may help to tackle procrastination and optimize AP.

Introduction

Procrastination is characterised by the postponement of engaging in, or the premature termination or completion of, an activity (or activities) pursued to achieve a goal (e.g., Fernie et al., 2016). In a sample drawn from the populations of six different nations (Australia, Peru, Spain, the United Kingdom, the United States, and Venezuela), the prevalence of ‘arousal’ procrastination (driven by a desire for more excitement and less boredom) was 13.5% and 14.3% for ‘avoidant’ procrastination (motivated by task aversiveness) amongst adults (Ferrari et al., 2016). The prevalence of chronic procrastination in students has been reported to be even higher: for example, Day et al. (2014) estimated rates of 32%. This is problematic given the findings of a recent meta-analysis that reported a negative relationship between procrastination and academic performance (Kim and Seo, 2015). However, procrastination is not only harmful to academic performance, but also to mental well-being: e.g., it is significantly associated with anxiety and depression (e.g., Spada et al., 2006a, Stöber and Joormann, 2001).

Procrastination may not always be problematic; instead, it can reflect an adaptive marshalling of resources and lead to better outcomes. To this end, procrastination has been variously delineated into two subtypes: e.g., functional and dysfunctional (Ferrari et al., 1995), active and passive (Chu and Choi, 2005), and intentional and unintentional (Fernie et al., 2016). Despite these different terminologies sharing many overlapping characteristics, there are important and nuanced differences. For example, intentional procrastination (IP) refers to a deliberate and conscious (i.e., active), but not necessarily advantageous (i.e., functional), behaviour. Whilst unintentional procrastination (UP) refers to a non-deliberate behaviour that is typically both dysfunctional and passive. UP has a stronger positive association with negative affect than IP (Fernie et al., 2016), supporting the discriminate validity of these two subtypes of procrastination.

For a little over a decade, several studies have investigated procrastination from a metacognitive perspective (de Palo et al., 2017, Fernie et al., 2017, Fernie et al., 2016, Fernie et al., 2015, Fernie and Spada, 2008, Fernie et al., 2009, Spada et al., 2006a). Metacognitions (or metacognitive beliefs) are defined as beliefs that individuals hold (both implicitly and explicitly) about their own attentional strategies, behaviours, repetitive thinking processes (e.g., rumination and worry), and emotions. These studies employed the Self-Regulatory Executive Function (S-REF; Wells and Matthews, 1994, Wells and Matthews, 1996) model as a framework to better understand procrastination. The Cognitive Attentional Syndrome (CAS) is key to building clinical formulations using the S-REF model. The CAS consists of a selection of cognitive processes (e.g., rumination, self-focused attention, and worry). According to the S-REF model, psychological disorder/distress occurs when metacognitive beliefs activate and maintain perseverative CAS configurations.

Metacognitive beliefs have been broadly delineated into positive and negative subtypes. For example, a positive metacognitive belief about procrastination is “Procrastination allows creativity to occur more naturally”, whilst a negative metacognitive belief is “My procrastination is uncontrollable” (Fernie et al., 2009). Positive metacognitive beliefs about procrastination are positively associated with IP and (less so) with UP, whilst negative metacognitive beliefs about procrastination are more strongly positively associated with UP than IP (Fernie et al., 2017, Fernie et al., 2016).

Recently, a metacognitive model of procrastination (based on the S-REF model) was tested and explained 46% of the variance in UP (Fernie et al., 2017). This model conceptualises UP, and to a lesser extent IP, as components of a CAS. In this model, an individual who strongly endorses positive metacognitive beliefs about procrastination is likely to activate IP as a coping strategy to deal with being given a task. IP is positively correlated with UP (Fernie et al., 2017, Fernie et al., 2016). It is likely challenging to engage solely in IP without slipping into UP. If the individual strongly endorses negative metacognitive beliefs about procrastination, UP (and IP) will be assessed as harmful, dangerous, and/or uncontrollable. Such appraisals will lead to worsening mood (Fernie et al., 2017, Fernie et al., 2016). To cope (i.e., to self-regulate their emotional functioning), CAS components are activated, including distraction, rumination, and worry. These processes are ‘resource heavy’ and contribute to cognitive or ‘ego’ depletion (Baumeister et al., 2000, Muraven and Baumeister, 2000). The activation of this CAS configuration means the individual's mental resources are mainly allocated to IP, UP, distraction, rumination, and worry processes. Consequently, the individual no longer has enough mental capacity to complete the original task. This paucity of mental resources makes more UP unavoidable. This aligns with a key conceptualisation of the S-REF model: i.e., psychological distress is a consequence of perseverative processes, such as UP.

This study had two objectives. Firstly, we sought to replicate the findings of earlier studies regarding the relationships between positive and negative metacognitive beliefs about procrastination, depressed mood, IP, and UP (e.g., Fernie et al., 2017, Fernie et al., 2016). Secondly, we aimed to test the metacognitive model of procrastination's ability to explain the mechanisms underlying the relationship between procrastination and academic performance. The current study operationalized these objectives with five experimental hypotheses (with hypotheses 1 to 3 addressing the first objective and hypotheses 4 and 5 the second). We hypothesised that: (1) positive metacognitive beliefs about procrastination would be positively and significantly related to IP and (less strongly) to UP, (2) negative metacognitive beliefs about procrastination would be positively and significantly associated with UP, (3) UP would have a stronger positive relationship with depressed mood than IP, (4) positive and negative metacognitive beliefs about procrastination would have significant and negative indirect effects on self-reported academic performance, and (5) the metacognitive model of procrastination, using self-reported academic performance as the dependent variable, would be a good fit of the data.

Section snippets

Participants

Study eligibility criteria required that participants: (1) were at least 18 years of age, (2) were current undergraduate or postgraduate students, (3) had received at least one assessment for a piece of coursework or exam for their current course within the last 12 months, (4) possessed adequate English language skills, and (5) consented to participate. Two hundred and forty-six (191 female) participants were initially recruited from students at King's College London and the University of

Normality tests and correlation analyses

Kolmogorov-Smirnov tests revealed the distribution of age, IDP, UPS, PHQ-8, PMP, NMP, and AP data were all significantly different from normal. Spearman's rho analyses generated the correlation matrix shown in Table 1. These analyses were used to test the first three of the study's hypotheses. They revealed that PMP was more strongly positively and significantly associated with IDP than UPS (hypothesis 1), NMP was positively and significantly related to UPS (hypothesis 2), and UPS was

Discussion

The results supported all five of the studies hypotheses. Firstly, PMP were positively and significantly related to IDP and (less strongly) to UPS. Secondly, NMP were positively and significantly associated with UPS. Thirdly, UPS had a stronger positive relationship with PHQ-8 than IDP. Fourthly, PMP and NMP had a negative and significant effect on AP. Fifthly, the metacognitive model of procrastination (with AP as the dependent variable) was an excellent fit of the data. Whilst this current

Compliance with ethical standards

This study involved human participants. All procedures performed in this study were conducted in accordance with the ethical standards of the institutional research committee and with the 1964 Helsinki declaration and its later amendments or comparable ethical standards. Informed consent was obtained from all individual participants included in the study.

Acknowledgements

Author BAF receives salary support from the National Institute for Health Research (NIHR) Mental Health Biomedical Research Centre and Dementia Research Unit at South London and Maudsley NHS Foundation Trust and King’s College London. The views expressed are those of the author and not necessarily those of the NHS, the NIHR, or the Department of Health.

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