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Record W4391903437 · doi:10.24251/hicss.2023.753

Innovating within Institutional Voids: A Digital Health Platform in India

2023· article· en· W4391903437 on OpenAlexaff
Suchit Ahuja, Yolande E. Chan, Arman Sadreddin

Bibliographic record

VenueProceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System Sciences · 2023
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInnovation and Socioeconomic Development
Canadian institutionsMcGill UniversityConcordia University
Fundersnot available
KeywordsIntermediaryFraming (construction)Cognitive reframingSustainabilityBusinessExtant taxonIndustrial organizationNudge theoryLegislatureWork (physics)Supply chainPsychological resilienceKnowledge managementMarketingComputer sciencePolitical scienceEngineering

Abstract

fetched live from OpenAlex

Most of the literature on digital innovation assumes availability of resources and access to markets and intermediaries. Institutional voids – lack of formal and informal arrangements – are generally seen as detrimental to digital innovation. While the extant literature provides insights about how some innovation can take place within institutional voids, it largely ignores the role of digital platforms. Based on field work in India, we examine how digital platforms can interface with institutional voids to create social and economic impacts. We find that platforms can address socio-economic challenges by framing, aggregating, and networking within institutional voids. Using an illustrative case study in rural India, where voids and constraints are prevalent, our research highlights how platforms can take strategic actions to develop socio-digital solutions to serve marginalized populations while earning sustainable revenues. We highlight dynamic interactions among physical, social, and digital layers that help platforms reframe constraints and address institutional voids.

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)0.005
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Scholarly communication, Open science
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.959
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0050.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0020.004
Science and technology studies0.0010.002
Scholarly communication0.0020.005
Open science0.0060.001
Research integrity0.0000.001
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 head0.054
GPT teacher head0.290
Teacher spread0.236 · 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
Published2023
Admission routes1
Has abstractyes

Explore more

Same venueProceedings of the ... Annual Hawaii International Conference on System Sciences/Proceedings of the Annual Hawaii International Conference on System SciencesSame topicInnovation and Socioeconomic DevelopmentFrench-language works237,207