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Record W2957564160

From hierarchies to markets: Transformation of corporate innovation at Ericsson

2019· article· en· W2957564160 on OpenAlexaff
Ning Su, Mayur Joshi, Saeed Khanagha

Bibliographic record

VenueJournal of the Association for Information Systems · 2019
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicBig Data and Business Intelligence
Canadian institutionsWestern University
Fundersnot available
KeywordsLeverage (statistics)Digital transformationBusinessCloud computingIndustrial organizationOpenness to experienceKnowledge managementCommoditizationThe InternetDisruptive technologyMarketingComputer scienceEngineeringEconomics
DOInot available

Abstract

fetched live from OpenAlex

This multi-year, in-depth qualitative case study examines an incumbent’s response to digital disruptions characterized by uncertain technologies and emergent business models, a relatively underexplored topic in Information Systems and Strategic Management literatures. In particular, the study documents Ericsson’s journey of transformation of corporate innovation in response to a wave of disruptive innovations, including cloud computing, Internet of Things, and the 5th Generation technologies coupled with changing customer requirements, increasing competition, and evolving industry landscape. The findings demonstrate how organizations can transform their corporate innovation strategy by employing a “long-tail” strategy as an openness imperative, thereby integrating a market of innovations within the traditional hierarchy of corporate R&D. This evolving hybrid model helps firms leverage the uncertainty during the period of digital disruption to their advantage.

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 machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.009
metaresearch head score (Gemma)0.010
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Qualitative · Consensus signal: Qualitative
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.015
Threshold uncertainty score0.106

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0090.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0020.001
Science and technology studies0.0130.011
Scholarly communication0.0110.006
Open science0.0010.007
Research integrity0.0030.004
Insufficient payload (model declined to judge)0.0040.001

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 head0.041
GPT teacher head0.252
Teacher spread0.211 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designQualitative
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
Published2019
Admission routes1
Has abstractyes

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