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Record W4417001198 · doi:10.54692/amr.2024.118

Navigating Digital Landscapes: Cross-Cultural Perspectives on Cyberspace in Business

2024· article· W4417001198 on OpenAlexaff
Amera Kiran Iqbal, Usman Riaz Mir

Bibliographic record

VenueAnnals of Management Review · 2024
Typearticle
Language
FieldSocial Sciences
TopicCybersecurity and Cyber Warfare Studies
Canadian institutionsNovelis (Canada)
Fundersnot available
KeywordsCyberspaceScope (computer science)PaceControl (management)Best practiceSustainable developmentSustainable business

Abstract

fetched live from OpenAlex

Cyberspace requires minimal cost and resources for an initial business setup, offering significant opportunities for automation, virtualization, AI-powered growth, higher productivity, convenience, and promising profits with a broad scope for development. It also facilitates green computing and sustainable practices for businesses worldwide, making it environmentally friendly and aligned with the 17 UN Sustainable Development Goals for fostering a peaceful and prosperous society. However, the threats posed by the integration of cyberspace into the business sector cannot be overlooked. These include the high demands on personal time and professional expertise required to establish and maintain online goodwill. Additionally, there is a lack of a standardized international regulatory framework for cyberspace activities, compounded by rising cyber-crime rates. The rapid pace of AI-driven evolution surpasses average human intelligence, raising concerns about control and trust in AI products among many businesses. While not all cyberspace-related threats can be entirely mitigated, businesses may adopt effective strategies to reduce potential harm. This research offers valuable insights for practitioners to integrate cyberspace effectively into the business world.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.727
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.003
Science and technology studies0.0000.001
Scholarly communication0.0010.001
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 head0.048
GPT teacher head0.414
Teacher spread0.366 · 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 designNot applicable
Domainnot available
GenreReview

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

Citations0
Published2024
Admission routes1
Has abstractyes

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