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Coworking Spaces: Catalysts of Workplace Revolution and Organizational Evolution

2025· article· en· W4416001877 on OpenAlexaff
Sven M. Laudien, Ricarda B. Bouncken, Anu Suominen, Anahita Baregheh, Ute Reuter, Katja-Maria Prexl

Bibliographic record

VenueAcademy of Management Proceedings · 2025
Typearticle
Languageen
FieldPsychology
TopicFacilities and Workplace Management
Canadian institutionsNipissing University
Fundersnot available
KeywordsTransformative learningValue (mathematics)Knowledge sharingWork (physics)Resource (disambiguation)Knowledge economy

Abstract

fetched live from OpenAlex

This symposium delves into the relevance, mechanisms, and transformative potential of coworking spaces. These shared work environments, equipped with essential infrastructure, attract individuals, start-ups, and established firms, serving as hubs for knowledge exchange, entrepreneurial activities, as well as innovation. By fostering collaboration mainly through physical co-presence, coworking spaces align with the open innovation paradigm while emphasizing community dynamics over organizational boundaries. Unlike incubation centers, coworking spaces cultivate vibrant ecosystems that facilitate resource sharing, knowledge exchange, and entrepreneurial opportunity recognition through collective reflection and inspiration. However, they also pose competitive risks such as opportunistic behavior, highlighting a need for maintaining a delicate balance between collaboration and competition. Despite their growing prominence, scholarly exploration of coworking spaces remains still limited. This panel symposium aims to advance the understanding of coworking spaces as catalysts for entrepreneurial activities and organizational change. By critically examining how various types of coworking spaces shape cooperation, entrepreneurial activities, and transformative processes, the symposium will explore their role in balancing the complexities of shared value creation and competition.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
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 score0.834
Threshold uncertainty score0.660

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
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 head0.011
GPT teacher head0.267
Teacher spread0.256 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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