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Record W4392816894 · doi:10.21203/rs.3.rs-4095131/v1

Understanding the Impact of Emerging Technologies on Entrepreneurial Ventures

2024· preprint· en· W4392816894 on OpenAlexaff
Samantha Reynolds

Bibliographic record

VenueResearch Square · 2024
Typepreprint
Languageen
FieldBusiness, Management and Accounting
TopicPrivate Equity and Venture Capital
Canadian institutionsKellogg's (Canada)
Fundersnot available
KeywordsNew VenturesBusinessEntrepreneurshipEmerging technologiesIndustrial organizationComputer scienceFinance

Abstract

fetched live from OpenAlex

Abstract In an era characterized by rapid technological advancement, the intersection of emerging technologies and entrepreneurial ventures has become a focal point of inquiry for scholars, policymakers, and practitioners alike. This qualitative research seeks to explore the multifaceted impact of emerging technologies on entrepreneurial endeavors, shedding light on the motivations, challenges, strategies, and outcomes associated with technology-driven entrepreneurship. Through in-depth interviews with entrepreneurs operating in diverse industries and geographical regions, this study elucidates the complex dynamics shaping the relationship between emerging technologies and entrepreneurial ventures. The findings reveal a diverse array of motivations driving entrepreneurs to adopt and integrate emerging technologies into their ventures, including economic incentives, social impact objectives, and environmental sustainability goals. However, the integration of emerging technologies is not without its challenges, as entrepreneurs encounter regulatory uncertainties, organizational resistance, and resource constraints in their quest to innovate. Despite these challenges, entrepreneurs employ various strategies, such as forming strategic partnerships, fostering innovation culture, and embracing customer-centric approaches, to successfully integrate emerging technologies into their ventures and drive innovation. The implications of these findings extend to both theory and practice in technology-driven entrepreneurship, highlighting the need for policymakers, investors, and practitioners to create an enabling environment that supports innovation and entrepreneurship. By understanding the drivers, barriers, and strategies associated with technology adoption in entrepreneurial ventures, stakeholders can better support and capitalize on the transformative potential of emerging technologies to drive economic growth and societal progress. In summary, this research contributes to our understanding of the complex interplay between emerging technologies and entrepreneurial ventures, offering insights for researchers, policymakers, and practitioners seeking to navigate the evolving landscape of technology-driven entrepreneurship.

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)0.001
metaresearch head score (Gemma)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesResearch integrity
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.200
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0010.000
Open science0.0010.006
Research integrity0.0000.002
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 head0.175
GPT teacher head0.400
Teacher spread0.226 · 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 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

Citations1
Published2024
Admission routes1
Has abstractyes

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