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Record W2595389828 · doi:10.24251/hicss.2017.501

A Meta-Analysis of Enjoyment Effect on Technology Acceptance: The Moderating Role of Technology Conventionality

2017· article· en· W2595389828 on OpenAlexaff
Nour El Shamy, Khaled Hassanein

Bibliographic record

VenueProceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsMcMaster University
Fundersnot available
KeywordsAffordanceWearable technologyComputer scienceHuman–computer interactionInformation technologyWearable computerTechnology acceptance modelEmerging technologiesInternet privacyKnowledge managementUsabilityArtificial intelligence

Abstract

fetched live from OpenAlex

Recent advancements in Information and Communication Technology lead to the development of affordable, novel, out of the ordinary, and unconventional information technology artifacts. Such innovative technologies including virtual reality, wearable technology, and robots; feature unique human-computer interfaces, untraditional hardware designs, enable unique and atypical affordances, and provide their users with unprecedented experiences. As these artifacts become more pervasive, it is important to understand whether established Information Systems theories apply to this new paradigm. This meta-analysis introduces the definition of technology conventionality and investigates its moderating role on the effect of perceived enjoyment on users’ behavioural intention to use the technology with the aim of contrasting the effect sizes across conventional and unconventional technologies. Findings indicate that perceived enjoyment plays an important role in shaping users’ behavioural intention for both conventional and unconventional technologies. Implications for practice and future research are discussed.

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)0.011
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Science and technology studies, Open science
Consensus categoriesScience and technology studies
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.909
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0110.001
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.002
Bibliometrics0.0030.004
Science and technology studies0.0020.007
Scholarly communication0.0010.002
Open science0.0210.003
Research integrity0.0010.001
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 head0.157
GPT teacher head0.397
Teacher spread0.240 · 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; both teacher heads agree on what is shown here.

Study designTheoretical or conceptual
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

Citations26
Published2017
Admission routes1
Has abstractyes

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