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Record W3113359588 · doi:10.17722/ijme.v16i1.1179

A framework of digitalization: Insights based on resource orchestration theory for digital transformation of traditional retailers

2020· article· en· W3113359588 on OpenAlexvenueno aff
Huang Qiu Bo, Dong Zi Guang, Jiang Yun Feng

Bibliographic record

VenueInternational Journal of Management Excellence · 2020
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicService and Product Innovation
Canadian institutionsnot available
Fundersnot available
KeywordsOrchestrationResource (disambiguation)Process managementCompetition (biology)Digital transformationEnterprise resource planningComputer scienceOffensiveBusinessKnowledge managementWorld Wide WebOperations researchEngineering

Abstract

fetched live from OpenAlex

The purpose of this article is to explore how traditional retailers implement overall organization digitalization. A cross-case study based on grounded theory was conducted across four traditional retail enterprise cases, respectively from comprehensive supermarket, department store, brand exclusive chain and home appliance chain. Four digitalization tactics dimensions were obtained: physical resource orchestration, human resource orchestration, organizational structure orchestration and ecological relationship orchestration; two digital competition strategies were distinguished: conservative and offensive strategy. Thereby, based on the resource orchestration theory, a procedural strategy framework was developed, which can be used to guide the implementation of digitalization.

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)0.003
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.006
Threshold uncertainty score0.043

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0030.002
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0020.002
Science and technology studies0.0020.023
Scholarly communication0.0060.011
Open science0.0010.004
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0040.000

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 head0.041
GPT teacher head0.239
Teacher spread0.199 · 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 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

Citations3
Published2020
Admission routes1
Has abstractyes

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