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Record W6913061040 · doi:10.5445/ir/1000143869

Predicting Procrastination in Everyday Life - From Individual Differences in Procrastination Tendencies to Intraindividual Variability in Momentary Task Appraisal

2022· book· en· W6913061040 on OpenAlexaboutno aff

Bibliographic record

VenueRepository KITopen (Karlsruhe Institute of Technology) · 2022
Typebook
Languageen
FieldPsychology
TopicPerfectionism, Procrastination, Anxiety Studies
Canadian institutionsnot available
Fundersnot available
KeywordsProcrastinationImpulsivityTask (project management)Everyday lifeAffect (linguistics)

Abstract

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Procrastination is typically defined as an irrational behavior characterized by unnecessarily delaying the completion of important tasks, contrary to one’s original intention, despite knowing that doing so could be to one’s detriment (cf. Klingsieck, 2013; Simpson & Pychyl, 2009; Steel, 2007). Taking a trait-based perspective, research has consistently shown that students’ self-reported procrastination tendencies are closely related to individual differences in conscientiousness, neuroticism, or impulsivity (Ferrari & Emmons, 1995; Lee et al., 2006; Schouwenburg & Lay, 1995; Watson, 2001). At the same time, procrastination can be understood as a task-specific avoidance behavior resulting from a failure of self-regulation (DeWitte & Lens, 2000; Howell et al., 2006; Steel et al., 2018). An effort to understand the occurrence of procrastination behavior as a failure of self-regulation would require to consider that individuals’ self-regulatory capacities — their motivational and volitional determinants — may change over time depending on task- or context-specific influences (see Dietrich et al., 2017; Martin et al., 2015; Vancouver & Kendall, 2006; Wäschle et al., 2014). Accordingly, research that seeks to identify the conditions that result in a failure to act in line with one’s intentions (i.e., the conditions that lead to the occurrence of an intention-action gap; Sheeran & Webb, 2016) will need to go beyond the examination of individual differences in procrastination tendencies. Accounting for individual differences in students’ procrastination behavior that can be attributed to trait-based determinants, the primary purpose of the present dissertation was to determine intra-individual mechanisms that affect the actual occurrence of procrastination behavior in real-life academic situations. To capture momentary changes (i.e., within-person changes) in motivational and volitional determinants that precede the occurrence of procrastination behavior, an event-based experience sampling approach was developed and implemented in a total of three studies. Study 1 (Chapter 2) set out to examine whether the occurrence of behavioral delays (the occurrence of an intention-action gap) would be predicted by withinperson changes in students’ cognitive-affective appraisals of tasks that arise between successive stages of goal-directed action. For one week, N = 75 students used an electronic diary (e-diary) to indicate their intentions to work on academic tasks (582 tasks planned) and their task-related appraisals (expectation to realize their intention, task value, and task aversiveness, and the effort required) each evening. For each intended task, a second assessment requested the next day determined whether students’ task-related appraisals changed, and whether they realized their intention on time or delayed working on the task (21.2% delays based on 501 completed task-specific measurements). Stepwise two-level logistic regression analyses revealed that lower expectations of success (i.e., ratings falling below an individual’s average) predicted an increased probability for task-specific delays. The risk that a task was delayed increased significantly when within-person changes in students’ appraisals indicated a devaluation (i.e., decreases in task-value, and increases in taskaversiveness). Students’ general procrastination tendencies that have been assessed at baseline have not significantly contributed to explain their individual delay behavior. To more accurately determine whether the occurrence of a behavioral delay should to be interpreted as procrastination behavior, a new 5-item short scale (the ecological Momentary Assessment of Procrastination Scale, e-MAPS) was developed and tested for its psychometric properties in Study 2 (Chapter 3). The applicability of the e-MAPS was tested in an experience sampling study with N = 80 students who were instructed to schedule at least two tasks they intended to work on for each of 17 days. At the time they intended to realize their intentions (2651 tasks planned), students were asked whether they worked on their task, or delayed working contrary to their original intention. If they had delayed working on their task (231 delays reported by 65 participants), participants were asked to complete the e-MAPS. An exploratory factor analysis revealed that the e-MAPS items covered two latent components, supporting the preconception that both situational and cognitive-affective determinants were relevant to classify a delay as procrastination behavior (25.5% of the delays classified as procrastination). A confirmatory factor analysis indicated that individual differences in delay patterns were assessed reliably. Associations between individuals’ aggregate frequencies of procrastination behavior captured by the e-MAPS, and their procrastination tendencies captured at baseline using two established self-report scales, support the convergent validity of the new scale. Study 3 (Chapter 4) extended the findings presented in Study 1 using the eMAPS to examine the influence that students’ perceptions of task-related ambiguity (i.e., uncertainty about actions or means required to successfully accomplish a task) had on the occurrence of procrastination behavior in studying for an exam. Questionnaires assessing students’ (N = 88) procrastination tendency and conscientiousness at baseline were combined with an adaptive experience sampling approach to assess students’ task-related perceptions of ambiguity and their situation-specific procrastination behavior during exam preparation six times a day for seven days (3581 measurements completed). Results revealed that 30% of 2286 intended study sessions were procrastinated. Study sessions were significantly more likely to be procrastinated when the momentary task-related ambiguity perception exceeded an individual’s average ambiguity perception (i.e., averaged across all intended study sessions). Students with pronounced procrastination tendencies were more likely, while more conscientious students were less likely to procrastinate study sessions. However, students’ conscientiousness explained virtually no variance in their procrastination behavior that was not explained by their general procrastination tendency. There was no indication for individual differences in the effect of ambiguity perceptions on the risk for procrastinating study sessions that could have been explained by students’ general procrastination tendency or conscientiousness. Two main conclusions can be drawn from the studies presented: First, it seems pertinent to consider trait-based determinants and more task- or contextspecific fluctuations in students’ self-regulatory capacities as complementary in their influence on the occurrence of procrastination behavior. Second, our findings highlight the ongoing imperative to examine procrastination behavior not only in terms of a general trait-based behavioral tendency, but also as a behavior that unfolds over time. Moreover, the latter requires to account for the fact that not every delay of an intended action should be considered an instance of procrastination. The use of trait- and state-based measurement approaches represents a major strength of the studies included in this dissertation. The implementation of an innovative experience sampling approach provided insights into the temporal instability of students’ intentions to initiate taskrelated actions, thus extending the available knowledge about intra-individual mechanisms that contribute to the occurrence of procrastination behavior. Further implications for research and practice will be discussed (Chapter 5).

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How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.031
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.003
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.000
Bibliometrics0.0040.002
Science and technology studies0.0000.001
Scholarly communication0.0000.001
Open science0.0020.001
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.021
GPT teacher head0.279
Teacher spread0.258 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations0
Published2022
Admission routes1
Has abstractyes

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