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Record W4406491801 · doi:10.1386/tmsd_00094_1

The impact of platform business models on organizational performance in the era of Industry 5.0: A strategic agility and innovation approach

2024· article· en· W4406491801 on OpenAlexaff
Mohamed Ashmel Mohamed Hashim, Issam Tlemsani, Osama El-Temtamy

Bibliographic record

VenueInternational Journal of Technology Management and Sustainable Development · 2024
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicDigital Platforms and Economics
Canadian institutionsMount Royal University
Fundersnot available
KeywordsBusinessProcess managementIndustrial organizationBusiness modelKnowledge managementMarketingComputer science

Abstract

fetched live from OpenAlex

The study explores the impact of the platform business model (PBM) on organizational performance, focusing on Industry 5.0 (I5.0). It introduces a conceptual model to assess how PBMs shape organizational outcomes. PBMs are recognized for their role in managing multi-sided markets and leveraging forces such as strategic agility (SA), business model innovation (BMI), strategic operations practice (SOP) and I5.0 technologies to create value. Despite their significance, the systematic integration of PBMs in the organizational value-creation process remains underexplored. To address this, the authors propose a pragmatic conceptual model, which is then converted into a structural equation model to evaluate the effects of PBMs on performance. Structural Equation Model (SEM), combined with confirmatory factor analysis, highlights PBMs’ intangible influence, driven by digital resources and evolving customer value propositions. PBMs face rapid changes influenced by SA, BMI, SOP and I5.0, leading to short-term competitive advantages. The study underscores the importance of SA, BMI and SOP in organizational strategy but notes their limited long-term benefits. It also presents research propositions with implications for leaders and policy-makers, enhancing the understanding of PBMs in the I5.0 context.

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: Theoretical or conceptual
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.236
Threshold uncertainty score0.277

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.001
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 head0.018
GPT teacher head0.228
Teacher spread0.210 · 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

Citations2
Published2024
Admission routes1
Has abstractyes

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