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Record W2746978892

ASSESSING THE INFLUENCE OF PERSUASIVE SYSTEMS FOR SUSTAINABILITY ACROSS WORK-HOME-COMMUNITY BOUNDARIES

2017· article· en· W2746978892 on OpenAlexaff
Jacqueline Corbett, Sarah Cherki El Idrissi

Bibliographic record

VenueJournal of the Association for Information Systems · 2017
Typearticle
Languageen
FieldEngineering
TopicErgonomics and Human Factors
Canadian institutionsUniversité Laval
Fundersnot available
KeywordsSustainabilityWork (physics)Computer scienceKnowledge managementEngineeringMechanical engineering
DOInot available

Abstract

fetched live from OpenAlex

Little doubt remains that human activities have contributed to climate change and environmental degradation over the past century. Humans must now alter their behaviours if the devastating consequences of climate change are to be avoided. To this end, persuasive systems for sustainability present a novel opportunity for encouraging environmentally responsible behaviours. This paper is part of ongoing research seeking to assess the influence of a persuasive system for sustainability deployed in one domain, such as a utility-sponsored energy conservation application, on individual behaviours within that domain, and also in other domains, such as at work or in the community. Here, we report on the development of measures to evaluate our constructs of interest, namely environmentally responsible behaviours at work, home and in the community, rebound effects, work-home-community boundary strengths, complexity of change and system characteristics of perceived persuasiveness and integration support. Through a process that included item creation, card sorting and exploratory factor analysis based on a survey of 168 participants, we have been successful in developing certain measures. Although still in progress, this work contributes to the Green IS literature by developing new measures and drawing attention to mechanisms for enhancing organizational and inter-organizational sustainability initiatives.

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)0.004
metaresearch head score (Gemma)0.002
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies, Scholarly communication
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.758
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0040.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.000
Scholarly communication0.0030.003
Open science0.0010.000
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 head0.019
GPT teacher head0.294
Teacher spread0.275 · 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".

Quick stats

Citations1
Published2017
Admission routes1
Has abstractyes

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