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Record W85974762

Conceptualizing User Satisfaction in the Ubiquitous Computing Era

2009· article· en· W85974762 on OpenAlexaff
Joanne Sullivan, Rens Scheepers, Catherine A. Middleton

Bibliographic record

VenueJournal of the Association for Information Systems · 2009
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsToronto Metropolitan University
Fundersnot available
KeywordsUbiquitous computingContext (archaeology)Computer scienceRealmSituational ethicsContext-aware pervasive systemsQuality (philosophy)Argument (complex analysis)Internet privacyPerspective (graphical)PerceptionData scienceKnowledge managementHuman–computer interactionPsychologySocial psychologyEpistemologyArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

In this research-in-progress paper we argue that technology in the ubiquitous computing era offers experiences to users that extend well beyond the functional, practical applications offered in the world of work. In this era a realm of engagement is opening up to the individual that transcends the utilitarian, to encompass hedonic and social existence. Our central argument, therefore, is that user satisfaction is a notion which must extend to encompass rich, holistic human experience involving complex and fleeting interactions, driven by highly personal circumstances. We argue that the expectations, requirements and value perceptions of individuals in this dynamic context may only be anticipated and understood if situational factors (such as location, time, context, history-of-use) and quality of life factors (such as life stage, mobility, health, income, background, education) are taken into account. We identify the fundamental differences in key characteristics of user satisfaction between the traditional and ubiquitous computing environments and provide details about our own research approach, in which we are exploring ubiquitous content provision from the perspective of content providers.

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)0.009
metaresearch head score (Gemma)0.003
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 score0.119
Threshold uncertainty score0.481

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0090.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.002
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 head0.058
GPT teacher head0.357
Teacher spread0.298 · 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

Citations4
Published2009
Admission routes1
Has abstractyes

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