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Record W4404342015 · doi:10.1016/j.im.2024.104057

Cutting corners as a coping strategy in information technology use: Unraveling the mind's dilemma

2024· article· en· W4404342015 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.
fundA Canadian funder is recorded on the work.

Bibliographic record

VenueInformation & Management · 2024
Typearticle
Languageen
FieldPsychology
TopicTechnostress in Professional Settings
Canadian institutionsMcMaster University
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsDilemmaCoping (psychology)PsychologyCognitive scienceCognitive psychologyEpistemologyEngineeringPhilosophyPsychotherapist

Abstract

fetched live from OpenAlex

Modern information technology (IT) features aimed at helping users can also increase the complexity of IT. The impact of this emergent complexity on employee behavior remains unknown. Using the transactional theory of stress, we propose that people cope with IT complexity by cutting corners. An experimental study involving 130 data analysts revealed (1) data analytics tools’ complexity increases distress, (2) distress fully mediates the impact of data analytics tools’ complexity on cognitive dissonance, (3) cutting corners negatively moderates distress–cognitive dissonance relationship, and (4) cognitive dissonance reduces perceived decision quality. These findings illuminate how employees navigate challenges using modern, complex IT.

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.

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)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.919
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.003
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.002

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 head0.323
Teacher spread0.304 · 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