Self-control strategies for financial goals: Proactive and reactive strategy use and effectiveness
Bibliographic record
Abstract
Self-control strategies help people resist tempting situations and make more goal-consistent decisions.I examined strategies in the financial domain through the lens of the Preventive-Interventive and Process models of self-control, distinguishing between proactive strategies used before a spending temptation and reactive strategies used during a spending temptation.I first conducted a meta-analysis to aggregate extant research and to estimate the overall effect size of financial self-control strategies.Strategies reduced spending and increased saving significantly with a medium effect size (d = 0.57, k = 29).I then examined whether these empirically studied strategies were present in a media sample (104 websites) and in people's personal experiences (n = 939).About half the strategies found through the meta-analysis were present in the media sample and were listed by lay participants, and across these three perspectives, the majority were proactive strategies.Next, I examined how strategy use impacted monthly spending in two longitudinal experimental studies.I asked participants to read about proactive vs. reactive strategies (Study 3) or list the proactive vs. reactive strategies they personally already use (Study 4).In Study 3, reminding participants of proactive or reactive strategies did not influence monthly spending compared to an empty control or a "just use willpower" condition.In Study 4, participants described their personal proactive and reactive strategies and watched a brief video highlighting relevant strategies.Participants who described proactive strategies reported spending $322 less than their goal during the month and significantly differed from the those who described reactive strategies (who spent $246 more than their goal) but did not differ significantly from an empty control condition.This finding suggests that people's personal proactive strategies can be effective for bringing spending in line with their goals.In sum, the first part of this dissertation summarizes and identifies gaps between the empirical literature, online media, and lay sample perspectives on self-control strategies for financial goals.The second part of this dissertation attempts to manipulate strategy use and assess proactive and reactive strategy effectiveness for bringing spending in line with goals.that I would not have been as productive or happy during my graduate school studies if I was with any other advisor.Her encouragement gave me the confidence to try new things and to challenge myself.I am inspired by her dedication to research and her amazing work ethic.I will fondly remember my time as her student, and I would like to think that Dr. Peetz will remain a mentor to me even after my PhD.I would also like to thank Dr. John Zelenski and Dr. Marina Milyavskaya.Our research collaborations have shaped my perspective as a researcher and have introduced me to new research techniques and ideas.I also wish to thank Dr. Elizabeth Dunn and Dr. Jose Rojas-Méndez for their feedback on this dissertation.I am deeply indebted to my parents, Mykola and Natalya Davydenko, who risked everything to come to Canada to invest in my future.Мама, спасибо за терпение и за бесчисленное часы мы вместе делали уроки.Папа, спасибо за твою поддержку и за то что ты пренуждав меня становиться лучше.Я не смогла бы сделать это без ваших жертв.Вы все, мама, папа, Таня и Коля, даёте мне сил ити дальше.Я бесконечно благодарна моей семье.During my graduate studies I was lucky enough to meet my best friend, my husband Timour Ibrahim.I am so thankful for his comfort during the tough times and his reassurance when my confidence wavered.Thank you for reading my drafts, listening to my presentations, and giving me the confidence to aim high.Especially during the last few months of writing this dissertation, his faith in me helped me persevere.I would not be where I am today without his understanding and encouragement.Finally, I would like
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.015 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.006 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".