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Record W2544290484

BYOD-enabled workarounds: A process perspective

2016· article· en· W2544290484 on OpenAlex
Alina Dulipovici, Dragos Vieru

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

VenueR-libre (Université Téluq) · 2016
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInformation Technology Governance and Strategy
Canadian institutionsUniversité TÉLUQHEC Montréal
FundersSocial Sciences and Humanities Research Council of Canada
KeywordsWorkaroundBring your own deviceProcess (computing)Perspective (graphical)Sociotechnical systemComputer scienceKnowledge managementMobile deviceWorld Wide WebArtificial intelligence
DOInot available

Abstract

fetched live from OpenAlex

Bring Your Own Device (BYOD) is a very popular trend in the corporate world despite some paradoxical characteristics and scarce research on this topic. Drawing on the theoretical lens of workarounds, this paper proposes a dynamic explanation of BYOD-enabled workarounds. Specifically, we develop a conceptual model representing the multi-level process model that occurs when an IS-enabled practice enacted with an organizational device is replaced by a BYOD-enabled workaround. We claim that three outcomes are then possible: status quo, reverting to the organizational practice, or legitimizing the BYOD-enabled workaround as the new organizational practice. Moreover, we explain the conditions that regulate the proposed model. 
\n
\nIn addition to addressing an important research gap, this study clarifies how and why several employers feel that they cannot prevent employees from using a BYOD approach. If a mix of conditions is already in place, there isn’t indeed much to do, but to embrace the reality.

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)0.000
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: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.667
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.005
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.001

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 head0.006
GPT teacher head0.180
Teacher spread0.174 · 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