MétaCan
Menu
Back to cohort
Record W3161432474 · doi:10.1145/3411764.3445619

Tailoring Persuasive and Behaviour Change Systems Based on Stages of Change and Motivation

2021· article· en· W3161432474 on OpenAlexafffund
Oladapo Oyebode, Chinenye Ndulue, Dinesh Mulchandani, Ashfaq Adib, Mona Alhasani, Rita Orji

Bibliographic record

Venuenot available
Typearticle
Languageen
FieldPsychology
TopicBehavioral Health and Interventions
Canadian institutionsDalhousie University
FundersNatural Sciences and Engineering Research Council of Canada
KeywordsPersuasive technologyBehaviour changePsychologyBehavior changeScale (ratio)PersuasionPersuasive communicationComputer scienceKnowledge managementSocial psychologyPsychological intervention

Abstract

fetched live from OpenAlex

Persuasive systems (PS) are effective at motivating behaviour change using various persuasive strategies. Research shows that tailoring these systems increases their effectiveness. However, there is little knowledge on how PS can be tailored to people's Stages of Change (SoC). We conduct a large-scale study of 568 participants to investigate how individuals at different SoC respond to various strategies. We also explore why the strategies motivate behaviour change using the ARCS motivation model. Our results show that people's SoC plays a significant role in the perceived persuasiveness of different strategies and that the strategies motivate for different reasons. For instance, people at the precontemplation stage tend to be strongly motivated by self-monitoring strategy because it raises their consciousness or self-awareness. Our work is the first to link research on the theory of SoC with the theory of motivation and Persuasive Systems Design (PSD) model to develop practical guidelines to inform the tailoring of persuasive systems.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.022
Threshold uncertainty score0.490

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.316
GPT teacher head0.409
Teacher spread0.093 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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".

Quick stats

Citations64
Published2021
Admission routes2
Has abstractyes

Explore more

Same topicBehavioral Health and InterventionsFrench-language works237,207