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Enregistrement 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 sur OpenAlexaboutno aff

Notice bibliographique

RevueRepository KITopen (Karlsruhe Institute of Technology) · 2022
Typebook
Langueen
DomainePsychology
ThématiquePerfectionism, Procrastination, Anxiety Studies
Établissements canadiensnon disponible
Organismes subventionnairesnon disponible
Mots-clésProcrastinationImpulsivityTask (project management)Everyday lifeAffect (linguistics)

Résumé

récupéré en direct d'OpenAlex

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).

Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.

Comment cette classification a été obtenuedéplier

Prédiction distillée sur la base complète

Imitation des enseignants

Ni prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.

score de la tête « metaresearch » (Codex)0,002
score de la tête « metaresearch » (Gemma)0,003
Version: codex-gemma-dda1882f352aStatut de validation: machine_predicted_unvalidated
Catégories candidatesMéta-épidémiologie (sens strict), Intégrité de la recherche
Catégories consensuellesaucune
DomaineSignal candidat: aucune · Signal consensuel: aucune
Devis d'étudeSignal candidat: Observationnel · Signal consensuel: Observationnel
GenreSignal candidat: Empirique · Signal consensuel: Empirique
Score de désaccord entre enseignants0,031
Score d'incertitude au seuil1,000

Scores Codex et Gemma par catégorie

CatégorieCodexGemma
Métarecherche0,0020,003
Méta-épidémiologie (sens strict)0,0010,001
Méta-épidémiologie (sens large)0,0020,000
Bibliométrie0,0040,002
Études des sciences et des technologies0,0000,001
Communication savante0,0000,001
Science ouverte0,0020,001
Intégrité de la recherche0,0010,003
Charge utile insuffisante (le modèle a refusé de juger)0,0000,000

Scores machine (provisoires)

Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.

Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.

Tête enseignante Opus0,021
Tête enseignante GPT0,279
Écart entre enseignants0,258 · la distance entre les deux têtes enseignantes sur ce seul travail
Statut de validationscore_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découle

Classification

machine, non validée

Prédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.

Devis d'étudeObservationnel
Domainenon disponible
GenreEmpirique

Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».

En bref

Citations0
Publié2022
Routes d'admission1
Résumé présentoui

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Même revueRepository KITopen (Karlsruhe Institute of Technology)Même sujetPerfectionism, Procrastination, Anxiety StudiesTravaux en français237 207