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Record W2606435195 · doi:10.5539/ibr.v10n5p73

Analyzing Cloud-based Startups: Evidence from a Case Study in Italy

2017· article· en· W2606435195 on OpenAlexvenueno aff
Luca Ferri, Marco Maffei, Gianluigi Mangia, Andrea Tomo

Bibliographic record

VenueInternational Business Research · 2017
Typearticle
Languageen
FieldDecision Sciences
TopicTechnology Adoption and User Behaviour
Canadian institutionsnot available
Fundersnot available
KeywordsCloud computingBusinessDiversification (marketing strategy)Industrial organizationProcess (computing)Information and Communications TechnologyKnowledge managementEntrepreneurshipProcess managementMarketingComputer scienceFinance

Abstract

fetched live from OpenAlex

The aim of this study is to analyze the reasons behind the adoption of cloud computing and its implementation process in startup firms as well as to verify the advantages and disadvantages deriving from the adoption of this tool and how it could increase entrepreneurial activities. We applied a research framework developed by previous scholars on cloud adoption within SMEs in an attempt to adapt it to startup firms. In particular, we conducted a case study in an Italian technological startup.Our results show that cloud technology supports and facilitates entrepreneurial activity, especially reducing several entry barriers for new entrepreneurs. This study contributes to the existing literature on cloud computing, and it has several managerial implications. First, it shows that setting up the organizational model on cloud computing allows entrepreneurs to reduce organizational efforts and ICT investments. Furthermore, this technology can reduce diversification costs by eliminating entry barriers, thus opening new markets and opportunities for entrepreneurs.

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)0.007
metaresearch head score (Gemma)0.023
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Scholarly communication, Insufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.017
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.023
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.001
Open science0.0030.001
Research integrity0.0000.001
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 head0.492
GPT teacher head0.563
Teacher spread0.072 · 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 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

Citations7
Published2017
Admission routes1
Has abstractyes

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